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  2025 (32)
On the caveats of AI autophagy. Xing, X.; Shi, F.; Huang, J.; Wu, Y.; Nan, Y.; Zhang, S.; Fang, Y.; Roberts, M.; Schönlieb, C., B.; Del Ser, J.; and Yang, G. Nature Machine Intelligence. 2 2025.
On the caveats of AI autophagy [pdf]Paper   doi   link   bibtex   abstract  
Enhancing Visual Reasoning with LLM-Powered Knowledge Graphs for Visual Question Localized-Answering in Robotic Surgery. Hao, P.; Wang, H.; Yang, G.; and Zhu, L. IEEE Journal of Biomedical and Health Informatics. 2025.
Enhancing Visual Reasoning with LLM-Powered Knowledge Graphs for Visual Question Localized-Answering in Robotic Surgery [pdf]Paper   link   bibtex  
Advancing breast, lung and prostate cancer research with federated learning. A systematic review. Ankolekar, A.; Boie, S.; Abdollahyan, M.; Gadaleta, E.; Hasheminasab, A.; Yang, G.; Beauville, C.; Dikaios, N.; Kastis, G., A.; Bussmann, M.; and others npj Digital Medicine. 2025.
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From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare. Li, M.; Xu, P.; Hu, J.; Tang, Z.; and Yang, G. Medical Image Analysis. 2025.
From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare [pdf]Paper   link   bibtex  
A Lung Structure and Function Information-Guided Residual Diffusion Model for Predicting Idiopathic Pulmonary Fibrosis Progression. Jiang, C.; Xing, X.; Yang, N.; Fang, Y.; Zhang, S.; Simon, W.; Yang, G.; and Shen, D. Medical Image Analysis. 2025.
A Lung Structure and Function Information-Guided Residual Diffusion Model for Predicting Idiopathic Pulmonary Fibrosis Progression [pdf]Paper   link   bibtex  
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023. Lyu, J.; Qin, C.; Wang, S.; Wang, F.; Li, Y.; Wang, Z.; Guo, K.; Ouyang, C.; Tänzer, M.; Liu, M.; and others Medical Image Analysis. 2025.
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023 [pdf]Paper   link   bibtex  
RVM+: An AI-Driven Vision Sensor Framework for High-Precision, Real-Time Video Portrait Segmentation with Enhanced Temporal Consistency and Optimized Model Design. Tang, N.; Liao, Y.; Chen, Y.; Yang, G.; Lai, X.; and Chen, J. Sensors. 2025.
RVM+: An AI-Driven Vision Sensor Framework for High-Precision, Real-Time Video Portrait Segmentation with Enhanced Temporal Consistency and Optimized Model Design [pdf]Paper   link   bibtex  
A Pipeline for Automated Quality Control of Chest Radiographs. Selby, I., A.; González Solares, E.; Breger, A.; Roberts, M.; Escudero Sánchez, L.; Babar, J.; Rudd, J., H., F.; Walton, N., A.; Sala, E.; Dittmer, S.; and others Radiology: Artificial Intelligence. 2025.
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Enhancing Diffusion-Weighted Images (DWI) for Diffusion MRI: is It Enough Without Non-Diffusion-Weighted b=0 Reference?. Wu, Y.; Huang, J.; Wang, F.; Gao, M.; Liao, C.; Yang, G.; and Setsompop, K. In IEEE International Symposium on Biomedical Imaging (ISBI 2025), 2025.
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Unpaired Translation of Chest X-ray Images for Lung Opacity Diagnosis via Adaptive Activation Masks and Cross-Domain Alignment. Ning, J.; Marshall, D.; Yijian, G.; Xiaodan, X.; Yang, N.; Yingying, F.; Zhang, S.; Komorowski, M.; and Yang, G. Pattern Recognition Letters. 2025.
Unpaired Translation of Chest X-ray Images for Lung Opacity Diagnosis via Adaptive Activation Masks and Cross-Domain Alignment [pdf]Paper   link   bibtex  
Bridging Multi-Level Gaps: Bidirectional Reciprocal Cycle Framework for Text- Guided Label-Efficient Segmentation in Echocardiography. Zhang, Z.; Zhang, H.; Zeng, T.; Yang, G.; Shi, Z.; and Gao, Z. Medical Image Analysis. 2025.
Bridging Multi-Level Gaps: Bidirectional Reciprocal Cycle Framework for Text- Guided Label-Efficient Segmentation in Echocardiography [pdf]Paper   link   bibtex  
Automated AI-based image analysis for quantification and prediction of Interstitial lung disease in systemic sclerosis patients. Guiot, J.; Henket, M.; Gester, F.; André, B.; Ernst, B.; Frix, A.; Smeets, D.; Van Eyndhoven, S.; Antoniou, K.; Conemans, L.; and others Respiratory Research. 2025.
Automated AI-based image analysis for quantification and prediction of Interstitial lung disease in systemic sclerosis patients [pdf]Paper   link   bibtex  
Revisiting Medical Image Retrieval via Knowledge Consolidation. Nan, Y.; Zhou, H.; Xing, X.; Papanastasiou, G.; Zhu, L.; Gao, Z.; Frangi, A.; and Yang, G. Medical Image Analysis. 2025.
Revisiting Medical Image Retrieval via Knowledge Consolidation [pdf]Paper   link   bibtex  
DMRN: A Dynamical Multi-order Response Network for the Robust Lung Airway Segmentation. Zhang, S.; Wu, J.; Ning, J.; and Yang, G. In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2025), 2025.
DMRN: A Dynamical Multi-order Response Network for the Robust Lung Airway Segmentation [pdf]Paper   link   bibtex  
Beyond the Hype: A dispassionate look at vision-language models in medical scenario. Nan, Y.; Zhou, H.; Xing, X.; and Yang, G. IEEE Transactions on Neural Networks and Learning Systems. 2025.
Beyond the Hype: A dispassionate look at vision-language models in medical scenario [pdf]Paper   link   bibtex  
Spreading Depolarization Detection in Electrocorticogram Spectrogram Imaging by Deep Learning: is It Just About Delta Band?. Boyer-Chammard, J.; Wu, Y.; Zhang, C.; Jewell, S.; Strong, A.; Yang, G.; and Boutelle, M. In IEEE International Symposium on Biomedical Imaging (ISBI 2025), 2025.
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Enhanced DTCMR with Cascaded Alignment and Adaptive Diffusion. Wang, F.; Luo, Y.; Munoz, C.; Wen, K.; Luo, Y.; Huang, J.; Wu, Y.; Khalique, Z.; Molto, M.; Rajakulasingam, R.; and others IEEE Transactions on Medical Imaging. 2025.
Enhanced DTCMR with Cascaded Alignment and Adaptive Diffusion [pdf]Paper   link   bibtex  
Deep learning image registration for cardiac motion estimation in adult and fetal echocardiography via a focus on anatomic plausibility and texture quality of warped image. Hasan, M., K.; Zhu, H.; Yang, G.; and Yap, C., H. Computers in Biology and Medicine. 2025.
Deep learning image registration for cardiac motion estimation in adult and fetal echocardiography via a focus on anatomic plausibility and texture quality of warped image [pdf]Paper   link   bibtex  
MP-FocalUNet: Multiscale parallel focal self-attention U-Net for medical image segmentation. Wang, C.; Jiang, M.; Li, Y.; Wei, B.; Li, Y.; Wang, P.; and Yang, G. Computer Methods and Programs in Biomedicine, 260(December 2024): 108562. 2025.
MP-FocalUNet: Multiscale parallel focal self-attention U-Net for medical image segmentation [link]Website   doi   link   bibtex   abstract  
Learning with noisy labels via Mamba and entropy KNN framework. Wang, N.; Jin, W.; Jing, S.; Bi, H.; and Yang, G. Applied Soft Computing, 169(November 2024): 112596. 2025.
Learning with noisy labels via Mamba and entropy KNN framework [link]Website   doi   link   bibtex   abstract  
Data augmentation strategies for semi-supervised medical image segmentation. Wang, J.; Ruan, D.; Li, Y.; Wang, Z.; Wu, Y.; Tan, T.; Yang, G.; and Jiang, M. Pattern Recognition, 159(928). 2025.
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AMVLM: Alignment-Multiplicity Aware Vision-Language Model for Semi-Supervised Medical Image Segmentation. Pan, Q.; Li, Z.; Qiao, W.; Lou, J.; Yang, Q.; Yang, G.; and Ji, B. IEEE Transactions on Medical Imaging, PP(Xx): 1-1. 2025.
AMVLM: Alignment-Multiplicity Aware Vision-Language Model for Semi-Supervised Medical Image Segmentation [link]Website   doi   link   bibtex  
A prompting multi-task learning-based veracity dissemination consistency reasoning augmentation for few-shot fake news detection. Jin, W.; Wang, N.; Tao, T.; Jiang, M.; Xing, Y.; Zhao, B.; Wu, H.; Duan, H.; and Yang, G. Engineering Applications of Artificial Intelligence, 144(May 2024): 110122. 2025.
A prompting multi-task learning-based veracity dissemination consistency reasoning augmentation for few-shot fake news detection [link]Website   doi   link   bibtex   abstract  
Artificial immunofluorescence in a flash: Rapid synthetic imaging from brightfield through residual diffusion. Xing, X.; Tang, C.; Murdoch, S.; Papanastasiou, G.; Guo, Y.; Xiao, X.; Cross-Zamirski, J.; Schönlieb, C., B.; Liang, K., X.; Niu, Z.; Fang, E., F.; Wang, Y.; and Yang, G. Neurocomputing, 612(October 2024): 128715. 2025.
Artificial immunofluorescence in a flash: Rapid synthetic imaging from brightfield through residual diffusion [link]Website   doi   link   bibtex   abstract  
Enhancing global sensitivity and uncertainty quantification in medical image reconstruction with Monte Carlo arbitrary-masked mamba. Huang, J.; Yang, L.; Wang, F.; Wu, Y.; Nan, Y.; Wu, W.; Wang, C.; Shi, K.; Aviles-Rivero, A., I.; Schönlieb, C., B.; Zhang, D.; and Yang, G. Medical Image Analysis, 99(August 2024): 103334. 2025.
Enhancing global sensitivity and uncertainty quantification in medical image reconstruction with Monte Carlo arbitrary-masked mamba [link]Website   doi   link   bibtex   abstract  
Artificial intelligence in drug development for delirium and Alzheimer's disease. Ai, R.; Xiao, X.; Deng, S.; Yang, N.; Xing, X.; Watne, L., O.; Selbæk, G.; Wedatilake, Y.; Xie, C.; Rubinsztein, D., C.; Palmer, J., E.; Neerland, B., E.; Chen, H.; Niu, Z.; Yang, G.; and Fang, E., F. Acta Pharmaceutica Sinica B, (xxx). 2025.
Artificial intelligence in drug development for delirium and Alzheimer's disease [link]Website   doi   link   bibtex   abstract  
Prognostic significance of myocardial fibrosis in men with alcoholic cardiomyopathy: insights from cardiac MRI. Li, S.; Zhuang, B.; Cui, C.; He, J.; Ren, Y.; Wang, H.; Francone, M.; Yang, G.; Mohiaddin, R.; Lu, M.; and Xu, L. European Radiology. 2025.
Prognostic significance of myocardial fibrosis in men with alcoholic cardiomyopathy: insights from cardiac MRI [link]Website   doi   link   bibtex   abstract  
Unveiling the Capabilities of Latent Diffusion Models for Classification of Lung Diseases in Chest X-Rays. Ning, J.; Xing, X.; Zhang, S.; Ma, X.; and Yang, G. Proceedings - International Symposium on Biomedical Imaging,1-5. 2025.
doi   link   bibtex   abstract  
CMRxRecon2024: A Multimodality, Multiview k-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI. Wang, Z.; Wang, F.; Qin, C.; Lyu, J.; Ouyang, C.; Wang, S.; Li, Y.; Yu, M.; Zhang, H.; Guo, K.; Shi, Z.; Li, Q.; Xu, Z.; Zhang, Y.; Li, H.; Hua, S.; Chen, B.; Sun, L.; Sun, M.; Li, Q.; Chu, Y.; Bai, W.; Qin, J.; Zhuang, X.; Prieto, C.; Young, A.; Markl, M.; Wang, H.; Wu, L.; Yang, G.; Qu, X.; and Wang, C. Radiology: Artificial Intelligence, 7(2). 3 2025.
CMRxRecon2024: A Multimodality, Multiview k-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI [link]Website   doi   link   bibtex   abstract  
Feedback Attention to Enhance Unsupervised Deep Learning Image Registration in 3D Echocardiography. Hasan, M., K.; Luo, Y.; Yang, G.; and Yap, C., H. IEEE Transactions on Medical Imaging, 44(5): 2230-2243. 2025.
doi   link   bibtex   abstract  
Learning Task-Specific Sampling Strategy for Sparse-View CT Reconstruction. Yang, L.; Huang, J.; Fang, Y.; Aviles-Rivero, A., I.; Schonlieb, C., B.; Zhang, D.; and Yang, G. IEEE Transactions on Instrumentation and Measurement, 74. 2025.
doi   link   bibtex   abstract  
Is Fitting Error a Reliable Metric for Assessing Deformable Motion Correction in Quantitative MRI?. Wang, F.; Wen, K.; Luo, Y.; Wu, Y.; Huang, J.; Pennell, D., J.; Ferreira, P., F.; Scott, A., D.; Nielles-Vallespin, S.; and Yang, G. Proceedings - International Symposium on Biomedical Imaging,1-5. 2025.
doi   link   bibtex   abstract  
  2024 (67)
Complex Neural Networks for Reconstructing Undersampled Spiral DiffusionTensor Cardiovascular Magnetic Resonance Data. Luo, Y.; Ferreira, P.; Pennell, D.; Yang, G.; Nielles-Vallespin, S.; and Scott, A. In Society for Cardiovascular Magnetic Resonance (SCMR) Annual Meeting, 2024.
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Deep Learning based Synthesis of MRI, CT and PET: Review and Analysis. Dayarathna, S.; Islam, K., T.; Uribe, S.; Yang, G.; Hayat, M.; and Chen, Z. Medical Image Analysis. 2024.
Deep Learning based Synthesis of MRI, CT and PET: Review and Analysis [pdf]Paper   link   bibtex   3 downloads  
CCheXR-Attention: Clinical Concept Extraction and Chest X-ray Reports Classification using Modified Mogrifier and Bidirectional LSTM with Multihead Attention. Rani, S.; Jain, A.; Kumar, A.; and Yang, G. International Journal of Imaging Systems and Technology. 2024.
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Optimized Post-processing for Diffusion Tensor Cardiac MRI with Texture-Preserving Deformable Alignment. Wang, F.; Ferreira, P.; Wu, Y.; Munoz, C.; Wen, K.; Luo, Y.; Huang, J.; Pennell, D., J.; Scott, A., D.; and Nielles-Vallespin, S. In Society for Cardiovascular Magnetic Resonance (SCMR) Annual Meeting, 2024.
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Efficient Post-processing of Diffusion Tensor Cardiac Magnetic Imaging Using Texture-conserving Deformable Registration. Wang, F.; Ferreira, P., F.; Wu, Y.; Munoz, C.; Wen, K.; Luo, Y.; Huang, J.; Pennell, D., J.; Scott, A., D.; and Nielles-Vallespin, S. In SPIE Medical Imaging 2024, 2024.
Efficient Post-processing of Diffusion Tensor Cardiac Magnetic Imaging Using Texture-conserving Deformable Registration [pdf]Paper   link   bibtex  
High-Resolution Reference Image Assisted Volumetric Super- Resolution of Cardiac Diffusion Weighted Imaging. Wu, Y.; Huang, J.; Wang, F.; Ferreira, P.; and Scott, A. In SPIE Medical Imaging 2024, 2024.
High-Resolution Reference Image Assisted Volumetric Super- Resolution of Cardiac Diffusion Weighted Imaging [pdf]Paper   link   bibtex  
SegmentAnything helps microscopy images based automatic and quantitative organoid detection and analysis. Xing, X.; Tang, C.; Guo, Y.; Kurniawan, N.; and Yang, G. In SPIE Medical Imaging 2024, 2024.
SegmentAnything helps microscopy images based automatic and quantitative organoid detection and analysis [pdf]Paper   SegmentAnything helps microscopy images based automatic and quantitative organoid detection and analysis [link]Website   link   bibtex   abstract  
Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19. Fang, Y.; Xing, X.; Wang, S.; Walsh, S.; and Yang, G. Current Opinion in Structural Biology. 2024.
Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19 [pdf]Paper   Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19 [link]Website   link   bibtex   abstract  
Dynamic Multimodal Information Bottleneck for Multimodality Classification. Fang, Y.; Wu, S.; Zhang, S.; Huang, C.; Zeng, T.; Xing, X.; Walsh, S.; and Yang, G. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2024, pages 7696-7706, 2024.
Dynamic Multimodal Information Bottleneck for Multimodality Classification [pdf]Paper   link   bibtex  
RCAR-UNet: Retinal vessel segmentation network algorithm via novel rough attention mechanism. Ding, W.; Sun, Y.; Huang, J.; Ju, H.; Zhang, C.; Yang, G.; and Lin, C., T. Information Sciences, 657(November 2023): 120007. 2024.
RCAR-UNet: Retinal vessel segmentation network algorithm via novel rough attention mechanism [pdf]Paper   RCAR-UNet: Retinal vessel segmentation network algorithm via novel rough attention mechanism [link]Website   doi   link   bibtex   abstract  
MLC: Multi-level consistency learning for semi-supervised left atrium segmentation. Shi, Z.; Jiang, M.; Li, Y.; Wei, B.; Wang, Z.; Wu, Y.; Tan, T.; and Yang, G. Expert Systems with Applications, 244(August 2023): 122903. 2024.
MLC: Multi-level consistency learning for semi-supervised left atrium segmentation [pdf]Paper   MLC: Multi-level consistency learning for semi-supervised left atrium segmentation [link]Website   doi   link   bibtex   abstract  
Laboratory data and broncho-alveolar lavage on Covid-19 patients with no intensive care unit admission: Correlation with chest CT features and clinical outcomes. Nardi, C.; Magnini, A.; Rastrelli, V.; Zantonelli, G.; Calistri, L.; Lorini, C.; Luzzi, V.; Gori, L.; Ciani, L.; Morecchiato, F.; Simonetti, V.; Peired, A., J.; Landini, N.; Cavigli, E.; Yang, G.; Guiot, J.; Tomassetti, S.; and Colagrande, S. Medicine (United States), 103(29): e39028. 7 2024.
Laboratory data and broncho-alveolar lavage on Covid-19 patients with no intensive care unit admission: Correlation with chest CT features and clinical outcomes [pdf]Paper   doi   link   bibtex   abstract  
Decoding Decision Reasoning: A Counterfactual-Powered Model for Knowledge Discovery. Fang, Y.; Jin, Z.; Xing, X.; Walsh, S.; and Yang, G. In Proceedings - International Symposium on Biomedical Imaging, 2024. IEEE Computer Society
Decoding Decision Reasoning: A Counterfactual-Powered Model for Knowledge Discovery [pdf]Paper   doi   link   bibtex   abstract  
Dual Consistency Regularization with Subjective Logic for Semi-Supervised Medical Image Segmentation. Lu, S.; Yan Ziye, C., W.; Cheng, T.; Zhang, Z.; and Yang, G. Computers in Biology and Medicine. 2024.
Dual Consistency Regularization with Subjective Logic for Semi-Supervised Medical Image Segmentation [pdf]Paper   link   bibtex  
Diff3Dformer: Leveraging Slice Sequence Diffusion for Enhanced 3D CT Classification with Transformer Networks. Jin, Z.; Fang, Y.; Xu, C.; Huang, J.; Simon, W.; and Yang, G. In Medical Image Computing and Computer Assisted Intervention (MICCAI 2024), 2024.
Diff3Dformer: Leveraging Slice Sequence Diffusion for Enhanced 3D CT Classification with Transformer Networks [pdf]Paper   link   bibtex  
Discovering Photoswitchable Molecules for Drug Delivery with Large Language Models and Chemist Instruction Training. Hu, J.; Wu, P.; Liu, Y.; Li, Y.; Li, Q.; Wang, S.; Qian, K.; and Yang, G. Pharmaceuticals. 2024.
Discovering Photoswitchable Molecules for Drug Delivery with Large Language Models and Chemist Instruction Training [pdf]Paper   link   bibtex  
Using Artificial Intelligence and Predictive Modelling to Enable Learning Healthcare Systems (LHS) for Pandemic Preparedness. Ankolekar, A.; Eppings, L.; Bottari, F.; Pinho, I.; Howard, K.; Baker, R.; Nan, Y.; Xing, X.; Walsh, S., L., F.; Vos, W.; and others Computational and Structural Biotechnology Journal. 2024.
Using Artificial Intelligence and Predictive Modelling to Enable Learning Healthcare Systems (LHS) for Pandemic Preparedness [pdf]Paper   link   bibtex  
US2Mask: Image-to-mask Generation Learning via a Conditional GAN for Cardiac Ultrasound Image Segmentation. Wang, G.; Ning, X.; Tiwari, P.; Zhu, H.; Yang, G.; and Yap, C., H. Computers in Biology and Medicine. 2024.
US2Mask: Image-to-mask Generation Learning via a Conditional GAN for Cardiac Ultrasound Image Segmentation [pdf]Paper   link   bibtex  
Where to Begin? From Random to Foundation Model Instructed Initialization in Federated Learning for Medical Image Segmentation. Li, M.; and Yang, G. In IEEE International Symposium on Biomedical Imaging (ISBI 2024), 2024.
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Adaptive Dynamic Inference for Few-Shot Left Atrium Segmentation. Chen, J.; Li, X.; Zhang, H.; Cho, Y.; Hwang, S., H.; Gao, Z.; and Yang, G. Medical Image Analysis. 2024.
Adaptive Dynamic Inference for Few-Shot Left Atrium Segmentation [pdf]Paper   link   bibtex  
Automated Segmentation of Brain Gliomas in Multimodal MRI Data. Xie, C.; Ye, J.; Ma, X.; Dong, L.; Zhao, G.; Cheng, J.; Yang, G.; and Lai, X. International Journal of Imaging Systems and Technology. 2024.
Automated Segmentation of Brain Gliomas in Multimodal MRI Data [pdf]Paper   link   bibtex  
A dual-task mutual learning framework for predicting post-thrombectomy cerebral hemorrhage. Jiang, C.; Wang, T.; Xing, X.; Liu, M.; Yang, G.; Ding, Z.; and Shen, D. In Medical Image Computing and Computer Assisted Intervention MICCAI 2024 International Workshop on Simulation and Synthesis in Medical Imaging, 2024.
A dual-task mutual learning framework for predicting post-thrombectomy cerebral hemorrhage [pdf]Paper   link   bibtex  
Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method. Wang, S.; Nan, Y.; Zhang, S.; Felder, F.; Xing, X.; Fang, Y.; Del Ser, J.; Walsh, S., L., F.; and Yang, G. Artificial Intelligence In Medicine. 2024.
Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method [pdf]Paper   link   bibtex  
A Smart Strategy for Photoresponsive Molecules: Utilizing Generative Pre-Trained Transformer and TDDFT Calculations in Drug Delivery. Hu, J.; Wu, P.; Li, Q.; Wang, S.; Xiao, X.; Niu, Z.; Wang, B.; and Yang, G. In International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC 2024), 2024.
A Smart Strategy for Photoresponsive Molecules: Utilizing Generative Pre-Trained Transformer and TDDFT Calculations in Drug Delivery [pdf]Paper   link   bibtex  
Hunting Imaging Biomarkers in Pulmonary Fibrosis: Benchmarks of the AIIB23 Challenge. Nan, Y.; Xing, X.; Wang, S.; Tang, Z.; Felder, F., N.; Zhang, S.; Ledda, R., E.; Ding, X.; Yu, R.; Liu, W.; and others Medical Image Analysis. 2024.
Hunting Imaging Biomarkers in Pulmonary Fibrosis: Benchmarks of the AIIB23 Challenge [pdf]Paper   link   bibtex  
DiffExplainer: Unveiling Black Box Models Via Counterfactual Generation. Fang, Y.; Wu, S.; Jin, Z.; Xu, C.; Wang, S.; Simon, W.; and Yang, G. In Medical Image Computing and Computer Assisted Intervention (MICCAI 2024), 2024.
DiffExplainer: Unveiling Black Box Models Via Counterfactual Generation [pdf]Paper   link   bibtex  
SOCR-YOLO: Small Objects Detection Algorithm In Medical Images. Liu, Y.; Li, Y.; Jiang, M.; Wang, S.; Ye, S.; Walsh, S.; and Yang, G. International Journal of Imaging Systems and Technology. 2024.
SOCR-YOLO: Small Objects Detection Algorithm In Medical Images [pdf]Paper   link   bibtex  
Fuzzy Attention-based Border Rendering Orthogonal Network for Lung Organ Segmentation. Zhang, S.; Fang, Y.; Nan, Y.; Wang, S.; Ding, W.; Ong, Y.; Frangi, A., F.; Pedrycz, W.; Walsh, S.; and Yang, G. IEEE Transactions on Fuzzy Systems. 2024.
Fuzzy Attention-based Border Rendering Orthogonal Network for Lung Organ Segmentation [pdf]Paper   link   bibtex  
HAMLET: Graph Transformer Neural Operator for Partial Differential Equations. Bryutkin, A.; Huang, J.; Deng, Z.; Yang, G.; Schönlieb, C.; and Aviles-Rivero, A. In International Conference on Machine Learning (ICML 2024), 2024.
HAMLET: Graph Transformer Neural Operator for Partial Differential Equations [pdf]Paper   link   bibtex  
Prediction of Ischemic Stroke Functional Outcomes from Acute-Phase Noncontrast CT and Clinical Information. Liu, Y.; Yu, Y.; Ouyang, J.; Jiang, B.; Ostmeier, S.; Wang, J.; Lu-Liang, S.; Yang, Y.; Yang, G.; Michel, P.; and others Radiology. 2024.
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PharmaBench: Enhancing ADMET Benchmarks with Large Language Models. Niu, Z.; Xiao, X.; Wu, W.; Cai, Q.; Jiang, Y.; Jin, W.; Wang, M.; Yang, G.; Kong, L.; Jin, X.; and others Nature Scientific Data. 2024.
PharmaBench: Enhancing ADMET Benchmarks with Large Language Models [pdf]Paper   link   bibtex  
Deep Learning based Synthesis of MRI, CT and PET: Review and Analysis. Dayarathna, S.; Islam, K., T.; Uribe, S.; Yang, G.; Hayat, M.; and Chen, Z. Medical Image Analysis. 2024.
Deep Learning based Synthesis of MRI, CT and PET: Review and Analysis [pdf]Paper   link   bibtex   3 downloads  
Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Study. Huang, J.; Ferreira, P., F.; Wang, L.; Wu, Y.; Aviles-Rivero, A., I.; Schonlieb, C.; Scott, A., D.; Khalique, Z.; Dwornik, M.; Rajakulasingam, R.; and others Nature Scientific Reports. 2024.
Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Study [pdf]Paper   link   bibtex  
Resp-BoostNet: Mental Stress Detection from Biomarkers Measurable by Smartwatches using Boosting Neural Network Technique. Kumar, S.; Chauhan, A., R.; Akhil; Kumar, A.; and Yang, G. IEEE Access. 2024.
Resp-BoostNet: Mental Stress Detection from Biomarkers Measurable by Smartwatches using Boosting Neural Network Technique [pdf]Paper   link   bibtex  
Customized T-time Inner Sampling Network with Uncertainty-aware Data Augmentation Strategy for Multi-annotated Lesion Segmentation. Zhou, X.; Wang, X.; Ma, H.; Zhang, J.; Wang, X.; Bai, X.; Zhang, L.; Long, J.; Chen, J.; Le, H.; and others Computers in Biology and Medicine. 2024.
Customized T-time Inner Sampling Network with Uncertainty-aware Data Augmentation Strategy for Multi-annotated Lesion Segmentation [pdf]Paper   link   bibtex  
CIResDiff: A Clinically-Informed Residual Diffusion Model for Predicting Idiopathic Pulmonary Fibrosis Progression. Jiang, C.; Xing, X.; Ou, Z.; Liu, M.; Simon, W.; Yang, G.; and Shen, D. In Medical Image Computing and Computer Assisted Intervention MICCAI 2024 International Workshop on Machine Learning in Medical Imaging, 2024.
CIResDiff: A Clinically-Informed Residual Diffusion Model for Predicting Idiopathic Pulmonary Fibrosis Progression [pdf]Paper   link   bibtex  
Groupwise Deformable Registration of Diffusion Tensor Cardiovascular Magnetic Resonance: Disentangling Diffusion Contrast, Respiratory and Cardiac Motions. Wang, F.; Luo, Y.; Wen, K.; Huang, J.; Ferreira, P., F.; Luo, Y.; Wu, Y.; Munoz, C.; Pennell, D., J.; Scott, A., D.; and others In Medical Image Computing and Computer Assisted Intervention (MICCAI 2024), 2024.
Groupwise Deformable Registration of Diffusion Tensor Cardiovascular Magnetic Resonance: Disentangling Diffusion Contrast, Respiratory and Cardiac Motions [pdf]Paper   link   bibtex  
Can Generative AI Replace Immunofluorescent Staining Processes? A Comparison Study of Synthetically Generated CellPainting Images from Brightfield. Xing, X.; Murdoch, S.; Tang, C.; Papanastasiou, G.; Cross-Zamirski, J.; Guo, Y.; Xiao, X.; Schönlieb, C.; Wang, Y.; and Yang, G. Computers in Biology and Medicine. 2024.
Can Generative AI Replace Immunofluorescent Staining Processes? A Comparison Study of Synthetically Generated CellPainting Images from Brightfield [pdf]Paper   link   bibtex  
Guest Editorial: Special Issue on the British Machine Vision Conference 2022. Yang, G.; Aviles-Rivero, A.; Fang, Y.; Feng, Z.; Ciocca, G.; Hicks, Y.; and Reyes-Aldasoro, C., C. International Journal of Computer Vision. 2024.
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Enhancing Weakly Supervised Semantic Segmentation for Fibrosis via Controllable Image Generation. Yue, Z.; Fang, Y.; Yang, L.; Baid, N.; Walsh, S.; and Yang, G. In arXiv preprint arXiv:2411.03551, 2024.
Enhancing Weakly Supervised Semantic Segmentation for Fibrosis via Controllable Image Generation [pdf]Paper   link   bibtex  
LGRNet: Local-Global Reciprocal Network for Uterine Fibroid Segmentation in Ultrasound Videos. Xu, H.; Yang, Y.; Aviles-Rivero, A.; Yang, G.; Qin, J.; and Zhu, L. In Medical Image Computing and Computer Assisted Intervention (MICCAI 2024), 2024.
LGRNet: Local-Global Reciprocal Network for Uterine Fibroid Segmentation in Ultrasound Videos [pdf]Paper   link   bibtex  
Automated molecular structure segmentation from documents using ChemSAM. Tang, B.; Niu, Z.; Wang, X.; Huang, J.; Ma, C.; Peng, J.; Jiang, Y.; Ge, R.; Hu, H.; Lin, L.; and others Journal of Cheminformatics, 16(1): 29. 2024.
Automated molecular structure segmentation from documents using ChemSAM [pdf]Paper   link   bibtex  
Labelling with Dynamics: A Data-efficient Learning Paradigm for Medical Image Segmentation. Mo, Y.; Liu, F.; Yang, G.; Wang, S.; Zheng, J.; Wu, F.; Papiez, B., W.; McIlwraith, D.; He, T.; and Guo, Y. Medical Image Analysis. 2024.
Labelling with Dynamics: A Data-efficient Learning Paradigm for Medical Image Segmentation [pdf]Paper   link   bibtex  
Video-Instrument Synergistic Network for Referring Video Instrument Segmentation in Robotic Surgery. Wang, H.; Yang, G.; Zhang, S.; Qin, J.; Guo, Y.; Xu, B.; Jin, Y.; and Zhu, L. IEEE Transactions on Medical Imaging. 2024.
Video-Instrument Synergistic Network for Referring Video Instrument Segmentation in Robotic Surgery [pdf]Paper   link   bibtex  
Unraveling the Distinction between Depression and Anxiety: A Machine Learning Exploration of Causal Relationships. Wang, T.; Xue, C.; Zhang, Z.; Cheng, T.; and Yang, G. Computers in Biology and Medicine. 2024.
Unraveling the Distinction between Depression and Anxiety: A Machine Learning Exploration of Causal Relationships [pdf]Paper   link   bibtex  
Real-Time Non-Invasive Imaging and Detection of Spreading Depolarizations through EEG: An Ultra-Light Explainable Deep Learning Approach. Wu, Y.; Jewell, S.; Xing, X.; Nan, Y.; Strong, A., J.; Yang, G.; and Boutelle, M., G. IEEE Journal of Biomedical and Health Informatics. 2024.
Real-Time Non-Invasive Imaging and Detection of Spreading Depolarizations through EEG: An Ultra-Light Explainable Deep Learning Approach [pdf]Paper   link   bibtex  
Performance of Artificial Intelligence in Detecting the Chronic Total Occlusive Lesions of Coronary Artery based on Coronary Computed Tomographic Angiography. Yang, Y.; Zhou, Z.; Zhang, N.; Wang, R.; Gao, Y.; Ran, X.; Sun, Z.; Zhang, H.; Yang, G.; Song, X.; and others Cardiovascular Diagnosis and Therapy. 2024.
Performance of Artificial Intelligence in Detecting the Chronic Total Occlusive Lesions of Coronary Artery based on Coronary Computed Tomographic Angiography [pdf]Paper   link   bibtex  
Distraction-aware hierarchical learning for vascular structure segmentation in intravascular ultrasound images. Zhong, W.; Zhang, H.; Gao, Z.; Hau, W., K.; Yang, G.; Liu, X.; and Xu, L. Computerized Medical Imaging and Graphics, 115(November 2023): 102381. 2024.
Distraction-aware hierarchical learning for vascular structure segmentation in intravascular ultrasound images [link]Website   doi   link   bibtex   abstract  
Can Rumor Detection Enhance Fact Verification? Unraveling Cross-Task Synergies Between Rumor Detection and Fact Verification. Jin, W.; Jiang, M.; Tao, T.; Zhou, H.; Wang, X.; Zhao, B.; and Yang, G. IEEE Transactions on Big Data, 11(3): 1171-1187. 2024.
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4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome. Wang, G.; Li, W.; Zhou, M.; Zhu, H.; Yang, G.; and Yap, C., H. CAAI Transactions on Intelligence Technology, (January): 1485-1499. 2024.
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Deep Asymmetric Mixture Model for Unsupervised Cell Segmentation. Nan, Y.; and Yang, G. Proceedings - International Symposium on Biomedical Imaging,1-5. 2024.
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Assessing the Efficacy of Invisible Watermarks in AI-Generated Medical Images. Xing, X.; Zhou, H.; Fang, Y.; and Yang, G. Proceedings - International Symposium on Biomedical Imaging, (c): 1-5. 2024.
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CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction across All Sampling Rates. Yang, L.; Huang, J.; Yang, G.; and Zhang, D. IEEE Transactions on Medical Imaging, PP(Xx): 1. 2024.
CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction across All Sampling Rates [link]Website   doi   link   bibtex   abstract  
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies. Huang, J.; Wu, Y.; Wang, F.; Fang, Y.; Nan, Y.; Alkan, C.; Xu, L.; Gao, Z.; Wu, W.; Zhu, L.; Chen, Z.; Lally, P.; Bangerter, N.; Setsompop, K.; Guo, Y.; Rueckert, D.; Wang, G.; and Yang, G. IEEE Reviews in Biomedical Engineering, 18: 152-171. 2024.
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies [link]Website   doi   link   bibtex   abstract  
Constraint-Aware Learning for Fractional Flow Reserve Pullback Curve Estimation from Invasive Coronary Imaging. Zhang, D.; Liu, X.; Wang, A.; Zhang, H.; Yang, G.; Zhang, H.; and Gao, Z. IEEE Transactions on Medical Imaging, 43(12): 4091-4104. 2024.
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Long-term clinical outcomes and cost-effectiveness of catheter vs thoracoscopic surgical ablation in long-standing persistent atrial fibrillation using continuous cardiac monitoring: CASA-AF randomized controlled trial. Boyalla, V.; Haldar, S.; Khan, H.; Kralj-Hans, I.; Banya, W.; Lord, J.; Satishkumar, A.; Bahrami, T.; De Souza, A.; Clague, J., R.; Francis, D., P.; Hussain, W.; Jarman, J., W.; Jones, D., G.; Chen, Z.; Mediratta, N.; Hyde, J.; Lewis, M.; Mohiaddin, R.; Salukhe, T., V.; Markides, V.; McCready, J.; Gupta, D.; Wong, T.; Yahdev, R.; Rahman-Halley, S.; Wong, J.; Opel, A.; Kaba, R.; Nyktari, E.; Cambronero-Cortinas, E.; Izgi, C.; Fairbairn, T.; Benton, J.; Chester, R.; Cunliffe, E.; Edmondson, L.; Gill, M.; Griffiths, V.; Harman, R.; Huggett, C.; Keegan, J.; Kirby, K.; Lascelles, K.; Manivarmane, R.; Munteanu, I.; O'Brien, K.; Phyl, T.; Rahneva, T.; Riley, C.; Rogers, P.; Smith, K.; Wage, R.; West, C.; Yakupoglu, Y.; Yang, G.; Audraite, A.; Belchambers, S.; Hughes, S.; Morgan, M.; Ronayne, C.; Shaw, R.; Simkus, P.; Taylerson, C.; Bruce, C.; McGregor, A.; Thomson, C.; Granville, H.; Snell, A.; John, S.; Monteiro, C.; Augustine, G.; and Sutton, N. Heart Rhythm, 21(9): 1562-1569. 2024.
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Feedback Attention for Unsupervised Cardiac Motion Estimation in 3D Echocardiography. Hasan, M., K.; Yang, G.; and Yap, C., H. Volume 15249 LNCS Springer Nature Switzerland, 2024.
Feedback Attention for Unsupervised Cardiac Motion Estimation in 3D Echocardiography [link]Website   doi   link   bibtex   abstract  
Variational Field Constraint Learning for Degree of Coronary Artery Ischemia Assessment. Zhang, Q.; Liu, X.; Zhang, H.; Xu, C.; Yang, G.; Yuan, Y.; Tan, T.; and Gao, Z. Volume 15003 LNCS Springer Nature Switzerland, 2024.
Variational Field Constraint Learning for Degree of Coronary Artery Ischemia Assessment [link]Website   doi   link   bibtex   abstract  
A veracity dissemination consistency-based few-shot fake news detection framework by synergizing adversarial and contrastive self-supervised learning. Jin, W.; Wang, N.; Tao, T.; Shi, B.; Bi, H.; Zhao, B.; Wu, H.; Duan, H.; and Yang, G. Scientific Reports, 14(1): 1-22. 2024.
A veracity dissemination consistency-based few-shot fake news detection framework by synergizing adversarial and contrastive self-supervised learning [link]Website   doi   link   bibtex   abstract  
CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI. Wang, C.; Lyu, J.; Wang, S.; Qin, C.; Guo, K.; Zhang, X.; Yu, X.; Li, Y.; Wang, F.; Jin, J.; Shi, Z.; Xu, Z.; Tian, Y.; Hua, S.; Chen, Z.; Liu, M.; Sun, M.; Kuang, X.; Wang, K.; Wang, H.; Li, H.; Chu, Y.; Yang, G.; Bai, W.; Zhuang, X.; Wang, H.; Qin, J.; and Qu, X. Scientific Data, 11(1): 1-9. 2024.
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A Human Feedback Strategy for Photoresponsive Molecules in Drug Delivery: Utilizing GPT-2 and Time-Dependent Density Functional Theory Calculations. Hu, J.; Wu, P.; Wang, S.; Wang, B.; and Yang, G. Pharmaceutics, 16(8). 2024.
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Fuzzy Attention-based Border Rendering Orthogonal Network for Lung Organ Segmentation. Zhang, S.; Fang, Y.; Nan, Y.; Wang, S.; Ding, W.; Ong, Y., S.; Frangi, A., F.; Pedrycz, W.; Walsh, S.; and Yang, G. IEEE Transactions on Fuzzy Systems, 32(10): 5462-5476. 2024.
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Dual-domain Collaborative Diffusion Sampling for Multi-Source Stationary Computed Tomography Reconstruction. Li, Z.; Chang, D.; Zhang, Z.; Luo, F.; Liu, Q.; Zhang, J.; Yang, G.; and Wu, W. IEEE Transactions on Medical Imaging, 43(10): 3398-3411. 2024.
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Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI. Wang, Z.; Wang, Z.; Xiao, M.; Zhou, Y.; Wang, C.; Wu, N.; Li, Y.; Gong, Y.; and Chang, S. , (February): 1-12. 2024.
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McCaD: Multi-Contrast MRI Conditioned, Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesis. Dayarathna, S.; Islam, K., T.; Zhuang, B.; Yang, G.; Cai, J.; Law, M.; and Chen, Z. 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV),670-679. 2024.
McCaD: Multi-Contrast MRI Conditioned, Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesis [link]Website   doi   link   bibtex   abstract  
Multi-level Noise Sampling from Single Image for Low-dose Tomography Reconstruction. Wu, W.; Long, Y.; Gao, Z.; Yang, G.; Cheng, F.; and Zhang, J. IEEE Journal of Biomedical and Health Informatics, 29(2): 1256-1268. 2024.
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Post-COVID highlights: Challenges and solutions of artificial intelligence techniques for swift identification of COVID-19. Fang, Y.; Xing, X.; Wang, S.; Walsh, S.; and Yang, G. Current Opinion in Structural Biology, 85(August 2023): 102778. 2024.
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A Survey, Review, and Future Trends of Skin Lesion Segmentation and Classification. Hasan, K.; Ahamad, M., A.; Yap, C., H.; and Yang, G. Computers in Biology and Medicine. 2023.
A Survey, Review, and Future Trends of Skin Lesion Segmentation and Classification [pdf]Paper   link   bibtex  
Navigating the Development Challenges in Creating Complex Data Systems. Dittmer, S.; Roberts, M.; Gilbey, J.; Biguri, A.; Selby, I.; Breger, A.; Thorpe, M.; Gilbey, J.; Weir-McCall, J., R.; and Gkrania-Klotsas, E. Nature Machine Intelligence. 2023.
Navigating the Development Challenges in Creating Complex Data Systems [pdf]Paper   link   bibtex  
Hierarchical Relational Inference for Few-Shot Learning in 3D Left Atrial Segmentation. Li, X.; Chen, J.; Zhang, H.; Cho, Y.; Hwang, S., H.; Gao, Z.; and Yang, G. IEEE Transactions on Emerging Topics in Computational Intelligence. 2023.
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Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation. Yeung, M.; Watts, T.; Tan, S., Y., W.; Ferreira, P., F.; Scott, A., D.; Nielles-Vallespin, S.; and Yang, G. arXiv preprint arXiv:2311.06552. 2023.
Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation [pdf]Paper   link   bibtex  
Deep Learning-Based Quantification of Traction Bronchiectasis Severity For Predicting Outcome in Idiopathic Pulmonary Fibrosis. Felder, F., N.; Nan, Y.; Yang, G.; Mackintosh, J.; Calandriello, L.; Goh, N.; Hopkins, P.; Moodley, Y.; Reynolds, P., N.; and Corte, T. European Respiratory Journal, 11(1): 76-78. 2023.
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Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation. Nan, Y.; Del Ser, J.; Tang, Z.; Tang, P.; Xing, X.; Fang, Y.; Herrera, F.; Pedrycz, W.; Walsh, S.; and Yang, G. IEEE Transactions on Neural Networks and Learning Systems. 2023.
Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation [pdf]Paper   link   bibtex  
Hybrid Swin Deformable Attention U-Net for Medical Image Segmentation. Wang, L.; Huang, J.; Xing, X.; and Yang, G. In International Symposium on Medical Information Processing and Analysis (SIPAIM), 2023.
Hybrid Swin Deformable Attention U-Net for Medical Image Segmentation [pdf]Paper   link   bibtex  
Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation. Xing, Z.; Wan, L.; Fu, H.; Yang, G.; and Zhu, L. arXiv preprint arXiv:2303.10326. 2023.
Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation [pdf]Paper   link   bibtex  
Outcome Prediction in Patients with Acute Ischemic Stroke by fusing MRI- based Deep Learning and Clinical Information. Liu, Y.; Ouyang, J.; Jiang, B.; Yu, Y.; Yang, G.; Liebeskind, D.; Lansberg, M.; Albers, G.; and Zaharchuk, G. In Annual Meeting of the American Society of Neuroradiology (ASNR 2023), 2023.
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Adversarial Transformer for Repairing Human Airway Segmentation. Tang, Z.; Yang, N.; Walsh, S.; and Yang, G. IEEE Journal of Biomedical and Health Informatics. 2023.
Adversarial Transformer for Repairing Human Airway Segmentation [pdf]Paper   link   bibtex  
A Pipeline to Further Enhance Quality, Integrity and Reusability of the NCCID Clinical Data. Breger, A.; Selby, I.; Roberts, M.; Babar, J.; Gkrania-Klotsas, E.; Preller, J.; Sánchez, L., E.; Dittmer, S.; Thorpe, M.; and Gilbey, J. Nature Scientific Data, 10(493). 2023.
A Pipeline to Further Enhance Quality, Integrity and Reusability of the NCCID Clinical Data [pdf]Paper   link   bibtex  
Hunting Imaging Biomarkers in Pulmonary Fibrosis: Benchmarks of the AIIB23 Challenge. Nan, Y.; Xing, X.; Wang, S.; Tang, Z.; Felder, F., N.; Zhang, S.; Ledda, R., E.; Ding, X.; Yu, R.; and Liu, W. arXiv preprint arXiv:2312.13752. 2023.
Hunting Imaging Biomarkers in Pulmonary Fibrosis: Benchmarks of the AIIB23 Challenge [pdf]Paper   link   bibtex  
High Accuracy and Cost-Saving Active Learning 3D WD-UNet for Airway Segmentation. Wang, S.; Nan, Y.; Walsh, S.; and Yang, G. In International Symposium on Medical Information Processing and Analysis (SIPAIM), 2023.
High Accuracy and Cost-Saving Active Learning 3D WD-UNet for Airway Segmentation [pdf]Paper   link   bibtex  
Improving Early Diagnosis of Primary Immunodeficiencies by Learning Causal Clinical History. Papanastasiou, G.; Ivanov, V.; Moore, S.; Sobolevsky, L.; Hsueh, S.; Xiang, J.; Aristeridou, D.; Kritharidou, M.; Tsarapatsanis, V.; and Yang, G. In Annual Symposium of American Medical Informatics Association (AMIA 2023), 2023.
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Fuzz-ClustNet: Coupled Fuzzy Clustering and Deep Neural Networks for Arrhythmia Detection from ECG Signals. Kumar, S.; Mallik, A.; Kumar, A.; Del Ser, J.; and Yang, G. Computers in Biology and Medicine. 2023.
Fuzz-ClustNet: Coupled Fuzzy Clustering and Deep Neural Networks for Arrhythmia Detection from ECG Signals [pdf]Paper   link   bibtex  
ChatAgri: Exploring Potentials of ChatGPT on Cross-linguistic Agricultural Text Classification. Zhao, B.; Jin, W.; Del Ser, J.; and Yang, G. Neurocomputing. 2023.
ChatAgri: Exploring Potentials of ChatGPT on Cross-linguistic Agricultural Text Classification [pdf]Paper   link   bibtex  
Multi-site, Multi-domain Airway Tree Modeling. Zhang, M.; Wu, Y.; Zhang, H.; Qin, Y.; Zheng, H.; Tang, W.; Arnold, C.; Pei, C.; Yu, P.; and Nan, Y. Medical Image Analysis. 2023.
Multi-site, Multi-domain Airway Tree Modeling [pdf]Paper   link   bibtex  
Late Breaking Abstract-Deep learning-based outcome prediction in pulmonary fibrosis using synthetic HRCT. Walsh, S.; Xing, X.; Mackintosh, J.; Calandriello, L.; Fang, Y.; Wang, S.; Zhang, S.; Nan, Y.; Silva, M.; and Wells, A. European Respiratory Journal. 2023.
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Deep Learning-based Prognostic Model Using Non-enhanced Cardiac Cine MRI for Outcome Prediction in Patients With Heart Failure. Gao, Y.; Zhou, Z.; Zhang, B.; Guo, S.; Bo, K.; Li, S.; Zhang, N.; Wang, H.; Yang, G.; and Zhang, H. European Radiology. 2023.
Deep Learning-based Prognostic Model Using Non-enhanced Cardiac Cine MRI for Outcome Prediction in Patients With Heart Failure [pdf]Paper   link   bibtex  
Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentations. Xing, X.; Papanastasiou, G.; Walsh, S.; and Yang, G. IEEE Transactions on Medical Imaging. 2023.
Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentations [pdf]Paper   link   bibtex  
Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey. Xing, X.; Wu, H.; Wang, L.; Stenson, I.; Yong, M.; Del Ser, J.; Walsh, S.; and Yang, G. ACM Computing Surveys. 2023.
Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey [pdf]Paper   link   bibtex  
Aortic Annulus Detection Based on Deep Learning for Transcatheter Aortic Valve Replacement Using Cardiac Computed Tomography. Cho, Y.; Park, S.; Hwang, S., H.; Ko, M.; Lim, D.; Yu, C., W.; Park, S.; Kim, M.; Oh, Y.; and Yang, G. Journal of Korean Medical Science. 2023.
Aortic Annulus Detection Based on Deep Learning for Transcatheter Aortic Valve Replacement Using Cardiac Computed Tomography [pdf]Paper   link   bibtex  
T1/T2 relaxation temporal modelling from accelerated acquisitions using a Latent Transformer. Wang, F.; Tanzer, M.; Qiao, M.; Bai, W.; Rueckert, D.; Yang, G.; and Nielles-Vallespin, S. In Medical Image Computing and Computer Assisted Intervention MICCAI 2023 International Workshop on Statistical Atlases and Computational Models of the Heart, 2023.
T1/T2 relaxation temporal modelling from accelerated acquisitions using a Latent Transformer [pdf]Paper   link   bibtex  
CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?. Huang, J.; Aviles-Rivero, A.; Schönlieb, C.; and Yang, G. In Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), 2023.
CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI? [pdf]Paper   link   bibtex  
Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Study. Huang, J.; Ferreira, P., F.; Wang, L.; Wu, Y.; Aviles-Rivero, A., I.; Schonlieb, C.; Scott, A., D.; Khalique, Z.; Dwornik, M.; and Rajakulasingam, R. arXiv preprint arXiv:2304.00996. 2023.
Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Study [pdf]Paper   link   bibtex  
Non-Invasive Prediction of Overall Survival Time for Glioblastoma Multiforme Patients Based on Multimodal MRI Radiomics. Zhu, J.; Ye, J.; Dong, L.; Ma, X.; Tang, N.; Xu, P.; Jin, W.; Li, R.; Yang, G.; and Lai, X. International Journal of Imaging Systems and Technology. 2023.
Non-Invasive Prediction of Overall Survival Time for Glioblastoma Multiforme Patients Based on Multimodal MRI Radiomics [pdf]Paper   link   bibtex  
Vehicular Abandoned Object Detection Based on VANET and Edge AI in Road Scenes. Wang, G.; Zhou, M.; Wei, X.; and Yang, G. IEEE Transactions on Intelligent Transportation Systems. 2023.
Vehicular Abandoned Object Detection Based on VANET and Edge AI in Road Scenes [pdf]Paper   link   bibtex  
GLRP: Global and Local Contrastive Learning Based on Relative Position for Medical Image Segmentation on Cardiac MRI. Zhao, X.; Wang, T.; Chen, J.; Jiang, B.; Li, H.; Zhang, N.; Yang, G.; and Chai, S. International Journal of Imaging Systems and Technology. 2023.
GLRP: Global and Local Contrastive Learning Based on Relative Position for Medical Image Segmentation on Cardiac MRI [pdf]Paper   link   bibtex  
Prompt Learning for Metonymy Resolution: Enhancing Performance with Internal Prior Knowledge of Pre-Trained Language Models. Zhao, B.; Jin, W.; Zhang, Y.; Huang, S.; and Yang, G. Knowledge-Based Systems. 2023.
Prompt Learning for Metonymy Resolution: Enhancing Performance with Internal Prior Knowledge of Pre-Trained Language Models [pdf]Paper   link   bibtex  
Mutually Aided Uncertainty Incorporated Dual Consistency Regularization with Pseudo Label for Semi-Supervised Medical Image Segmentation. Lu, S.; Zhang, Z.; Yan, Z.; Wang, Y.; Cheng, T.; Zhou, R.; and Yang, G. Neurocomputing. 2023.
Mutually Aided Uncertainty Incorporated Dual Consistency Regularization with Pseudo Label for Semi-Supervised Medical Image Segmentation [pdf]Paper   link   bibtex  
MRI Radiomics for Brain Metastasis Sub-pathology Classification From Non-small Cell Lung Cancer: a Machine Learning, Multicenter Study. Deng, F.; Liu, Z.; Fang, W.; Niu, L.; Chu, X.; Cheng, Q.; Zhang, Z.; Zhang, R.; and Yang, G. Physical and Engineering Sciences in Medicine. 2023.
MRI Radiomics for Brain Metastasis Sub-pathology Classification From Non-small Cell Lung Cancer: a Machine Learning, Multicenter Study [pdf]Paper   link   bibtex  
Motion Estimation Based on Projective Information Disentanglement for 3D Reconstruction of Rotational Coronary Angiography. Liu, X.; Li, S.; Wang, B.; Xu, L.; Gao, Z.; and Yang, G. Computers in Biology and Medicine. 2023.
Motion Estimation Based on Projective Information Disentanglement for 3D Reconstruction of Rotational Coronary Angiography [pdf]Paper   link   bibtex  
A Clinical and Imaging Fused Deep Learning Model Matches Expert Clinician Prediction of 90-Day Stroke Outcomes. Liu, Y.; Shah, P.; Yu, Y.; Horsey, J.; Ouyang, J.; Jiang, B.; Yang, G.; Heit, J., J.; McCullough, M., E.; and Hugdal, S., M. American Journal of Neuroradiology. 2023.
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Large-Kernel Attention for 3D Medical Image Segmentation. Li, H.; Nan, Y.; Del Ser, J.; and Yang, G. Cognitive Computation. 2023.
Large-Kernel Attention for 3D Medical Image Segmentation [pdf]Paper   link   bibtex  
Is Attention All You Need in Medical Image Analysis? A Review. Papanastasiou, G.; Dikaios, N.; Huang, J.; Wang, C.; and Yang, G. IEEE Journal of Biomedical and Health Informatics. 2023.
Is Attention All You Need in Medical Image Analysis? A Review [pdf]Paper   link   bibtex  
Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution Segmentation. Wang, L.; Huang, J.; Xing, X.; Wu, Y.; Rajakulasingam, R.; Scott, A., D.; Ferreira, P., F.; De Silva, R.; Nielles-Vallespin, S.; and Yang, G. In International Symposium on Medical Information Processing and Analysis (SIPAIM), 2023.
Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution Segmentation [pdf]Paper   link   bibtex  
Real-Time Non-Invasive Imaging and Detection of Spreading Depolarizations through EEG: An Ultra-Light Explainable Deep Learning Approach. Wu, Y.; Jewell, S.; Xing, X.; Nan, Y.; Strong, A., J.; Yang, G.; and Boutelle, M., G. arXiv preprint arXiv:2309.03147. 2023.
Real-Time Non-Invasive Imaging and Detection of Spreading Depolarizations through EEG: An Ultra-Light Explainable Deep Learning Approach [pdf]Paper   link   bibtex  
The Beauty or the Beast: Which Aspect of Synthetic Medical Images Deserves Our Focus. Xing, X.; Nan, Y.; Felder, F.; Walsh, S.; and Yang, G. In IEEE International Symposium on Computer-Based Medical Systems (CBMS 2023), 2023.
The Beauty or the Beast: Which Aspect of Synthetic Medical Images Deserves Our Focus [pdf]Paper   link   bibtex  
You Don’t Have to Be Perfect to Be Amazing: Unveil the Utility of Synthetic Images. Xing, X.; Felder, F.; Nan, Y.; Papanastasiou, G.; Simon, W.; and Yang, G. In Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), 2023.
You Don’t Have to Be Perfect to Be Amazing: Unveil the Utility of Synthetic Images [pdf]Paper   link   bibtex  
Large-Scale Analysis with Deep Learning for Early Diagnosis of Patients at Risk for Primary Immunodeficiencies. Papanastasiou, G.; Sagalovich, M.; Sidhu, G.; Sobolevsky, L.; Yang, G.; Fotiadis, D.; and Palumbo, D. In American Medical Informatics Association Summit 2023 (AMIA 2023), 2023.
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Editorial: Generative adversarial networks in cardiovascular research. Zhang, Q.; Cukur, T.; Greenspan, H.; and Yang, G. Frontiers in Cardiovascular Medicine, 10(October): 10-12. 2023.
Editorial: Generative adversarial networks in cardiovascular research [pdf]Paper   Editorial: Generative adversarial networks in cardiovascular research [link]Website   doi   link   bibtex  
Deep Learning-based Prediction of Percutaneous Recanalization in Chronic Total Occlusion Using Coronary CT Angiography. Zhou, Z.; Gao, Y.; Zhang, W.; Zhang, N.; Wang, H.; Wang, R.; Gao, Z.; Huang, X.; Zhou, S.; Dai, X.; Yang, G.; Zhang, H.; Nieman, K.; and Xu, L. Radiology, 309(2): e231149. 11 2023.
Deep Learning-based Prediction of Percutaneous Recanalization in Chronic Total Occlusion Using Coronary CT Angiography. [pdf]Paper   Deep Learning-based Prediction of Percutaneous Recanalization in Chronic Total Occlusion Using Coronary CT Angiography. [link]Website   doi   link   bibtex   abstract  
Multi-scale, Data-driven and Anatomically Constrained Deep Learning Image Registration for Adult and Fetal Echocardiography. Hasan, M., K.; Zhu, H.; Yang, G.; and Yap, C., H. arXiv preprint arXiv:2309.00831. 2023.
Multi-scale, Data-driven and Anatomically Constrained Deep Learning Image Registration for Adult and Fetal Echocardiography [pdf]Paper   Multi-scale, Data-driven and Anatomically Constrained Deep Learning Image Registration for Adult and Fetal Echocardiography [link]Website   link   bibtex   abstract  
Large-scale deep learning analysis to identify adult patients at risk for combined and common variable immunodeficiencies. Papanastasiou, G.; Yang, G.; Fotiadis, D., I.; Dikaios, N.; Wang, C.; Huda, A.; Sobolevsky, L.; Raasch, J.; Perez, E.; Sidhu, G.; and Palumbo, D. Communications Medicine, 3(1): 1-15. 2023.
Large-scale deep learning analysis to identify adult patients at risk for combined and common variable immunodeficiencies [pdf]Paper   doi   link   bibtex  
CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction. Wang, C.; Lyu, J.; Wang, S.; Qin, C.; Guo, K.; Zhang, X.; Yu, X.; Li, Y.; Wang, F.; Jin, J.; Shi, Z.; Xu, Z.; Tian, Y.; Hua, S.; Chen, Z.; Liu, M.; Sun, M.; Kuang, X.; Wang, K.; Wang, H.; Li, H.; Chu, Y.; Yang, G.; Bai, W.; Zhuang, X.; Wang, H.; Qin, J.; and Qu, X. arXiv preprint arXiv:2309.10836,1-14. 2023.
CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction [pdf]Paper   CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction [link]Website   link   bibtex   abstract  
Is Autoencoder Truly Applicable for 3d CT Super-Resolution?. Luo, W.; Xing, X.; and Yang, G. Proceedings - International Symposium on Biomedical Imaging, 2023-April: 1-4. 2023.
Is Autoencoder Truly Applicable for 3d CT Super-Resolution? [pdf]Paper   doi   link   bibtex   abstract  
Editorial: Advances in machine learning methods facilitating collaborative image-based decision making for neuroscience. Wang, C.; Zhang, H.; Papanastasiou, G.; and Yang, G. Frontiers in Computational Neuroscience, 17. 2023.
Editorial: Advances in machine learning methods facilitating collaborative image-based decision making for neuroscience [pdf]Paper   doi   link   bibtex  
ViGU: Vision GNN U-Net for fast MRI. Huang, J.; Aviles-Rivero, A., I.; Schonlieb, C., B.; and Yang, G. Proceedings - International Symposium on Biomedical Imaging, 2023-April: 11-15. 2023.
ViGU: Vision GNN U-Net for fast MRI [pdf]Paper   doi   link   bibtex   abstract  
Focus on machine learning models in medical imaging. Papanastasiou, G.; García Seco de Herrera, A.; Wang, C.; Zhang, H.; Yang, G.; and Wang, G. Physics in Medicine and Biology, 68(1): 0-4. 2023.
Focus on machine learning models in medical imaging [pdf]Paper   doi   link   bibtex  
Multiple Adversarial Learning Based Angiography Reconstruction for Ultra-Low-Dose Contrast Medium CT. Zhang, W.; Zhou, Z.; Gao, Z.; Yang, G.; Xu, L.; Wu, W.; and Zhang, H. IEEE Journal of Biomedical and Health Informatics, 27(1): 409-420. 2023.
Multiple Adversarial Learning Based Angiography Reconstruction for Ultra-Low-Dose Contrast Medium CT [pdf]Paper   doi   link   bibtex   abstract  
Artificial intelligence–based full aortic CT angiography imaging with ultra-low-dose contrast medium: a preliminary study. Zhou, Z.; Gao, Y.; Zhang, W.; Bo, K.; Zhang, N.; Wang, H.; Wang, R.; Du, Z.; Firmin, D.; Yang, G.; Zhang, H.; and Xu, L. European Radiology, 33(1): 678-689. 2023.
Artificial intelligence–based full aortic CT angiography imaging with ultra-low-dose contrast medium: a preliminary study [pdf]Paper   doi   link   bibtex   abstract  
The Missing U for Efficient Diffusion Models. Calvo-Ordonez, S.; Cheng, C.; Huang, J.; Zhang, L.; Yang, G.; Schonlieb, C.; and Aviles-Rivero, A., I. arXiv preprint arXiv:2310.20092. 10 2023.
The Missing U for Efficient Diffusion Models [pdf]Paper   The Missing U for Efficient Diffusion Models [link]Website   link   bibtex   abstract  
Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation. Tang, Z.; Xing, X.; and Yang, G. arXiv preprint arXiv:2307.10182, XXX(X): 1-11. 2023.
Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation [pdf]Paper   Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation [link]Website   link   bibtex   abstract  
Functional Outcome Prediction in Acute Ischemic Stroke Using a Fused Imaging and Clinical Deep Learning Model. Liu, Y.; Yu, Y.; Ouyang, J.; Jiang, B.; Yang, G.; Ostmeier, S.; Wintermark, M.; Michel, P.; Liebeskind, D., S.; Lansberg, M., G.; Albers, G., W.; and Zaharchuk, G. Stroke, 54(9): 2316-2327. 2023.
Functional Outcome Prediction in Acute Ischemic Stroke Using a Fused Imaging and Clinical Deep Learning Model [pdf]Paper   doi   link   bibtex   abstract  
Automatic COVID-19 and Common-Acquired Pneumonia Diagnosis Using Chest CT Scans. Motta, P., C.; Cortez, P., C.; Silva, B., R.; Yang, G.; and Albuquerque, V., H., C. Bioengineering, 10(5): 1-29. 2023.
Automatic COVID-19 and Common-Acquired Pneumonia Diagnosis Using Chest CT Scans [pdf]Paper   doi   link   bibtex   abstract  
Conditional Physics-Informed Graph Neural Network for Fractional Flow Reserve Assessment. Xie, B.; Liu, X.; Zhang, H.; Xu, C.; Zeng, T.; Yuan, Y.; Yang, G.; and Gao, Z. Volume 14226 LNCS Springer Nature Switzerland, 2023.
Conditional Physics-Informed Graph Neural Network for Fractional Flow Reserve Assessment [pdf]Paper   Conditional Physics-Informed Graph Neural Network for Fractional Flow Reserve Assessment [link]Website   doi   link   bibtex   abstract  
Pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction. Jiang, Y.; Jin, S.; Jin, X.; Xiao, X.; Wu, W.; Liu, X.; Zhang, Q.; Zeng, X.; Yang, G.; and Niu, Z. Communications Chemistry, 6(1). 2023.
Pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction [pdf]Paper   doi   link   bibtex   abstract  
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation. Zhang, M.; Wu, Y.; Zhang, H.; Qin, Y.; Zheng, H.; Tang, W.; Arnold, C.; Pei, C.; Yu, P.; Nan, Y.; Yang, G.; Walsh, S.; Marshall, D., C.; Komorowski, M.; Wang, P.; Guo, D.; Jin, D.; Wu, Y.; Zhao, S.; Chang, R.; Zhang, B.; Lv, X.; Qayyum, A.; Mazher, M.; Su, Q.; Wu, Y.; Liu, Y.; Zhu, Y.; Yang, J.; Pakzad, A.; Rangelov, B.; Estepar, R., S., J.; Espinosa, C., C.; Sun, J.; Yang, G.; and Gu, Y. arXiv preprint arXiv:2303.05745. 2023.
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation [pdf]Paper   Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation [link]Website   link   bibtex   abstract  
A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuning. Zhu, J.; Yang, G.; and Lio, P. arXiv preprint arXiv:2302.11184,1-28. 2023.
A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuning [pdf]Paper   A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuning [link]Website   link   bibtex   abstract  
Hierarchical Perception Adversarial Learning Framework for Compressed Sensing MRI. Gao, Z.; Guo, Y.; Zhang, J.; Zeng, T.; and Yang, G. IEEE Transactions on Medical Imaging, 42(6): 1859-1874. 2023.
Hierarchical Perception Adversarial Learning Framework for Compressed Sensing MRI [pdf]Paper   doi   link   bibtex   abstract  
Data-Free Distillation Improves Efficiency and Privacy in Federated Thorax Disease Analysis. Li, M.; and Yang, G. In IEEE EMBS International Conference on Data Science and Engineering in Healthcare, Medicine & Biology, 2023.
Data-Free Distillation Improves Efficiency and Privacy in Federated Thorax Disease Analysis [pdf]Paper   Data-Free Distillation Improves Efficiency and Privacy in Federated Thorax Disease Analysis [link]Website   link   bibtex   abstract  
The impact of imputation quality on machine learning classifiers for datasets with missing values. Shadbahr, T.; Roberts, M.; Stanczuk, J.; Gilbey, J.; Teare, P.; Dittmer, S.; Thorpe, M.; Torné, R., V.; Sala, E.; Lió, P.; Patel, M.; Preller, J.; Selby, I.; Breger, A.; Weir-McCall, J., R.; Gkrania-Klotsas, E.; Korhonen, A.; Jefferson, E.; Langs, G.; Yang, G.; Prosch, H.; Babar, J.; Escudero Sánchez, L.; Wassin, M.; Holzer, M.; Walton, N.; Lió, P.; Rudd, J., H., F.; Mirtti, T.; Rannikko, A., S.; Aston, J., A., D.; Tang, J.; and Schönlieb, C. Nature Communications Medicine, 3(1): 139. 2023.
The impact of imputation quality on machine learning classifiers for datasets with missing values [pdf]Paper   doi   link   bibtex   abstract  
The Missing U for Efficient Diffusion Models. Calvo-Ordonez, S.; Cheng, C.; Huang, J.; Zhang, L.; Yang, G.; Schonlieb, C.; and Aviles-Rivero, A., I. . 2023.
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Quantifying the Impact of Pyramid Squeeze Attention Mechanism and Filtering Approaches on Alzheimer's Disease Classification. Yan, B.; Li, Y.; Li, L.; Yang, X.; Li, T.; Yang, G.; and Jiang, M. Computers in Biology and Medicine. 2022.
Quantifying the Impact of Pyramid Squeeze Attention Mechanism and Filtering Approaches on Alzheimer's Disease Classification [pdf]Paper   link   bibtex  
Automatic Fine-grained Glomerular Lesion Recognition in Kidney Pathology. Nan, Y.; Li, F.; Tang, P.; Zhang, G.; Zeng, C.; Xie, G.; Liu, Z.; and Yang, G. Pattern Recognition. 2022.
Automatic Fine-grained Glomerular Lesion Recognition in Kidney Pathology [pdf]Paper   link   bibtex  
Swin Transformer for Fast MRI. Huang, J.; Fang, Y.; Wu, Y.; Wu, H.; Gao, Z.; Li, Y.; Del Ser, J.; Xia, J.; and Yang, G. Neurocomputing. 2022.
Swin Transformer for Fast MRI [pdf]Paper   link   bibtex  
Global Transformer and Dual Local Attention Network via Deep-Shallow Hierarchical Feature Fusion for Retinal Vessel Segmentation. Li, Y.; Zhang, Y.; Liu, J.; Wang, K.; Zhang, K.; Zhang, G.; Liao, X.; and Yang, G. IEEE Transactions on Cybernetics. 2022.
Global Transformer and Dual Local Attention Network via Deep-Shallow Hierarchical Feature Fusion for Retinal Vessel Segmentation [pdf]Paper   link   bibtex  
Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRI. Huang, J.; Xing, X.; Gao, Z.; and Yang, G. In Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), 2022.
Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRI [pdf]Paper   link   bibtex  
CHAIMELEON project: Creation of a pan-European repository of health imaging data for the development of AI-powered cancer management tools. Bonmatí, L., M.; Blanco, A., M.; Suárez, A.; Aznar, M.; Beregi, J., P.; Fournier, L.; Neri, E.; Laghi, A.; França, M.; and Sardanelli, F. Frontiers in Oncology. 2022.
CHAIMELEON project: Creation of a pan-European repository of health imaging data for the development of AI-powered cancer management tools [pdf]Paper   link   bibtex  
MCAL: An Anatomical Knowledge Learning Model for Myocardial Segmentation in 2D Echocardiography. Cui, X.; Zhang, P.; Li, Y.; Liu, Z.; Xiao, X.; Zhang, Y.; Sun, L.; Cui, L.; Yang, G.; and Li, S. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control. 2022.
MCAL: An Anatomical Knowledge Learning Model for Myocardial Segmentation in 2D Echocardiography [pdf]Paper   link   bibtex  
Fast MRI Reconstruction: How Powerful Transformers Are?. Huang, J.; Wu, Y.; Wu, H.; and Yang, G. In IEEE International Engineering in Medicine and Biology Conference (EMBC 2022), 2022.
Fast MRI Reconstruction: How Powerful Transformers Are? [pdf]Paper   link   bibtex  
CS2: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention. Xing, X.; Huang, J.; Nan, Y.; Wu, Y.; Wang, C.; Gao, Z.; Walsh, S.; and Yang, G. In Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), 2022.
CS2: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention [pdf]Paper   link   bibtex  
AI-based reconstruction for fast MRI—a systematic review and meta-analysis. Chen, Y.; Schönlieb, C.; Liò, P.; Leiner, T.; Dragotti, P., L.; Wang, G.; Rueckert, D.; Firmin, D.; and Yang, G. Proceedings of the IEEE, 110(2): 224-245. 2022.
AI-based reconstruction for fast MRI—a systematic review and meta-analysis [pdf]Paper   link   bibtex  
AI-based Medical e-Diagnosis for Fast and Automatic Ventricular Volume Measurement in the Patients with Normal Pressure Hydrocephalus. Zhou, X.; Ye, Q.; Yang, X.; Chen, J.; Ma, H.; Xia, J.; Del Ser, J.; and Yang, G. Neural Computing and Applications. 2022.
AI-based Medical e-Diagnosis for Fast and Automatic Ventricular Volume Measurement in the Patients with Normal Pressure Hydrocephalus [pdf]Paper   link   bibtex  
Calibrating the Dice Loss to Handle Neural Network Overconfidence for Biomedical Image Segmentation. Yeung, M.; Rundo, L.; Nan, Y.; Sala, E.; Schönlieb, C.; and Yang, G. Journal of Digital Imaging. 2022.
Calibrating the Dice Loss to Handle Neural Network Overconfidence for Biomedical Image Segmentation [pdf]Paper   link   bibtex  
Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels. Li, M.; Fang, Y.; Tang, Z.; Onuorah, C.; Xia, J.; Del Ser, J.; Walsh, S.; and Yang, G. IEEE Transactions on Emerging Topics in Computational Intelligence. 2022.
Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels [pdf]Paper   link   bibtex  
Influence of Co-morbidities during SARS-CoV-2 infection in an Indian Population. Matysek, A.; Studnicka, A.; Smith, W., M.; Hutny, M.; Gajewski, P.; Filipiak, K., J.; Goh, J.; and Yang, G. Frontiers in Medicine. 2022.
Influence of Co-morbidities during SARS-CoV-2 infection in an Indian Population [pdf]Paper   link   bibtex  
Quantification of Changes in White Matter Tract Fibers in Idiopathic Normal Pressure Hydrocephalus Based on Diffusion Spectrum Imaging. Yang, X.; Li, H.; He, W.; Lv, M.; Zhang, H.; Zhou, X.; Wei, H.; Xu, B.; Chen, J.; and Ma, H. European Journal of Radiology. 2022.
Quantification of Changes in White Matter Tract Fibers in Idiopathic Normal Pressure Hydrocephalus Based on Diffusion Spectrum Imaging [pdf]Paper   link   bibtex  
Medical Image Understanding and Analysis. Yang, G.; Aviles-Rivero, A.; Roberts, M.; and Schönlieb, C. 2022.
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HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis. Xing, X.; Del Ser, J.; Wu, Y.; Li, Y.; Xia, J.; Xu, L.; Firmin, D.; Gatehouse, P.; and Yang, G. IEEE Journal of Biomedical and Health Informatics. 2022.
HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis [pdf]Paper   link   bibtex  
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation. Li, H.; Nan, Y.; Del Ser, J.; and Yang, G. Neural Computing and Applications. 2022.
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation [pdf]Paper   link   bibtex  
Skin Lesion Analysis: A State-of-the-Art Survey, Systematic Review, and Future Trends. Hasan, M., K.; Ahamad, M., A.; Yap, C., H.; and Yang, G. 2022.
Skin Lesion Analysis: A State-of-the-Art Survey, Systematic Review, and Future Trends [pdf]Paper   link   bibtex  
From Astronomy to Histology: Adapting the Fellwalker Algorithm to Deep Nuclear Instance Segmentation. Yeung, M.; Watts, T.; and Yang, G. In Annual Conference on Medical Image Understanding and Analysis (MIUA 2022), 2022.
From Astronomy to Histology: Adapting the Fellwalker Algorithm to Deep Nuclear Instance Segmentation [pdf]Paper   link   bibtex  
Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond. Yang, G.; Ye, Q.; and Xia, J. Information Fusion, 77: 29-52. 2022.
Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond [pdf]Paper   doi   link   bibtex   abstract  
JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets. Chen, J.; Yang, G.; Khan, H.; Zhang, H.; Zhang, Y.; Zhao, S.; Mohiaddin, R.; Wong, T.; Firmin, D.; and Keegan, J. IEEE Journal of Biomedical and Health Informatics, 26(1): 103-114. 2022.
JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets [pdf]Paper   doi   link   bibtex   abstract  
Focal Attention Networks: Optimising Attention for Biomedical Image Segmentation. Yeung, M.; Rundo, L.; Sala, E.; Schonlieb, C.; and Yang, G. In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pages 1-5, 3 2022. IEEE
Focal Attention Networks: Optimising Attention for Biomedical Image Segmentation [pdf]Paper   Focal Attention Networks: Optimising Attention for Biomedical Image Segmentation [link]Website   doi   link   bibtex  
Accelerating Cardiac Diffusion Tensor Imaging With a U-Net Based Model: Toward Single Breath-Hold. Ferreira, P., F.; Banerjee, A.; Scott, A., D.; Khalique, Z.; Yang, G.; Rajakulasingam, R.; Dwornik, M.; De Silva, R.; Pennell, D., J.; Firmin, D., N.; and Nielles-Vallespin, S. Journal of Magnetic Resonance Imaging, 56(6): 1691-1704. 2022.
Accelerating Cardiac Diffusion Tensor Imaging With a U-Net Based Model: Toward Single Breath-Hold [pdf]Paper   doi   link   bibtex   abstract  
Edge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view information. Huang, J.; Ding, W.; Lv, J.; Yang, J.; Dong, H.; Del Ser, J.; Xia, J.; Ren, T.; Wong, S., T.; and Yang, G. Applied Intelligence, 52(13): 14693-14710. 2022.
Edge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view information [pdf]Paper   Edge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view information [link]Website   doi   link   bibtex   abstract  
LKAU-Net: 3D Large-Kernel Attention-Based U-Net for Automatic MRI Brain Tumor Segmentation. Li, H.; Nan, Y.; and Yang, G. Volume 3 . Annual Conference on Medical Image Understanding and Analysis 2022, pages 313-327. Springer International Publishing, 2022.
Annual Conference on Medical Image Understanding and Analysis 2022 [pdf]Paper   Annual Conference on Medical Image Understanding and Analysis 2022 [link]Website   doi   link   bibtex  
Generative Adversarial Network Powered Fast Magnetic Resonance Imaging—Comparative Study and New Perspectives. Yang, G.; Lv, J.; Chen, Y.; Huang, J.; and Zhu, J. Volume 217 . Generative Adversarial Learning: Architectures and Applications, pages 305-339. Springer International Publishing, 2022.
Generative Adversarial Learning: Architectures and Applications [pdf]Paper   Generative Adversarial Learning: Architectures and Applications [link]Website   doi   link   bibtex   abstract  
Robust weakly supervised learning for COVID-19 recognition using multi-center CT images. Ye, Q.; Gao, Y.; Ding, W.; Niu, Z.; Wang, C.; Jiang, Y.; Wang, M.; Fang, E., F.; Menpes-Smith, W.; Xia, J.; and Yang, G. Applied Soft Computing, 116: 108291. 2022.
Robust weakly supervised learning for COVID-19 recognition using multi-center CT images [pdf]Paper   Robust weakly supervised learning for COVID-19 recognition using multi-center CT images [link]Website   doi   link   bibtex   abstract  
Faster Diffusion Cardiac MRI with Deep Learning-Based Breath Hold Reduction. Tänzer, M.; Ferreira, P.; Scott, A.; Khalique, Z.; Dwornik, M.; Pennell, D.; Yang, G.; Rueckert, D.; and Nielles-Vallespin, S. Volume 3 . Medical Image Understanding and Analysis 2022, pages 101-115. 2022.
Medical Image Understanding and Analysis 2022 [pdf]Paper   Medical Image Understanding and Analysis 2022 [link]Website   doi   link   bibtex  
AI-Based Reconstruction for Fast MRI-A Systematic Review and Meta-Analysis. Chen, Y.; Schonlieb, C., B.; Lio, P.; Leiner, T.; Dragotti, P., L.; Wang, G.; Rueckert, D.; Firmin, D.; and Yang, G. Proceedings of the IEEE, 110(2): 224-245. 2022.
AI-Based Reconstruction for Fast MRI-A Systematic Review and Meta-Analysis [pdf]Paper   doi   link   bibtex   abstract  
Explainable AI (XAI) In Biomedical Signal and Image Processing: Promises and Challenges. Yang, G.; Rao, A.; Fernandez-Maloigne, C.; Calhoun, V.; and Menegaz, G. In 2022 IEEE International Conference on Image Processing (ICIP), pages 1531-1535, 10 2022. IEEE
Explainable AI (XAI) In Biomedical Signal and Image Processing: Promises and Challenges [pdf]Paper   Explainable AI (XAI) In Biomedical Signal and Image Processing: Promises and Challenges [link]Website   doi   link   bibtex  
3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework. Guan, X.; Yang, G.; Ye, J.; Yang, W.; Xu, X.; Jiang, W.; and Lai, X. BMC Medical Imaging, 22(1): 1-18. 2022.
3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework [pdf]Paper   3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework [link]Website   doi   link   bibtex   abstract  
Unsupervised Image Registration towards Enhancing Performance and Explainability in Cardiac and Brain Image Analysis. Wang, C.; Yang, G.; and Papanastasiou, G. Sensors, 22(6): 2125. 3 2022.
Unsupervised Image Registration towards Enhancing Performance and Explainability in Cardiac and Brain Image Analysis [pdf]Paper   Unsupervised Image Registration towards Enhancing Performance and Explainability in Cardiac and Brain Image Analysis [link]Website   doi   link   bibtex   abstract  
Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks. Kondylakis, H.; Ciarrocchi, E.; Cerda-Alberich, L.; Chouvarda, I.; Fromont, L., A.; Garcia-Aznar, J., M.; Kalokyri, V.; Kosvyra, A.; Walker, D.; Yang, G.; and Neri, E. European Radiology Experimental, 6(1): 1-15. 2022.
Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks [pdf]Paper   doi   link   bibtex   abstract  
Deep Learning-enabled Prostate Segmentation: Large Cohort Evaluation with Inter-Reader Variability Analysis. Liu, Y.; Qi, M.; Surawech, C.; Zheng, H.; Nguyen, D.; Yang, G.; Raman, S.; and Sung, K. In International Society for Magnetic Resonance in Medicine, pages 1-3, 2022.
Deep Learning-enabled Prostate Segmentation: Large Cohort Evaluation with Inter-Reader Variability Analysis [pdf]Paper   link   bibtex  
Review of Data Types and Model Dimensionality for Cardiac DTI SMS-Related Artefact Removal. Tänzer, M.; Yook, S., H.; Ferreira, P.; Yang, G.; Rueckert, D.; and Nielles-Vallespin, S. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13593 LNCS: 123-132. 2022.
Review of Data Types and Model Dimensionality for Cardiac DTI SMS-Related Artefact Removal [pdf]Paper   doi   link   bibtex   abstract  
Unsupervised Tissue Segmentation via Deep Constrained Gaussian Network. Nan, Y.; Tang, P.; Zhang, G.; Zeng, C.; Liu, Z.; Gao, Z.; Zhang, H.; and Yang, G. IEEE Transactions on Medical Imaging, 41(12): 3799-3811. 2022.
Unsupervised Tissue Segmentation via Deep Constrained Gaussian Network [pdf]Paper   doi   link   bibtex   abstract  
Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions. Nan, Y.; Del Ser, J.; Walsh, S., S.; Schönlieb, C.; Roberts, M.; Selby, I.; Howard, K.; Owen, J.; Neville, J.; Guiot, J.; Ernst, B.; Pastor, A.; Alberich-Bayarri, A.; Menzel, M., I.; Walsh, S., S.; Vos, W.; Flerin, N.; Charbonnier, J., P.; van Rikxoort, E.; Chatterjee, A.; Woodruff, H.; Lambin, P.; Cerdá-Alberich, L.; Martí-Bonmatí, L.; Herrera, F.; and Yang, G. Information Fusion, 82(December 2021): 99-122. 2022.
Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions [pdf]Paper   doi   link   bibtex   abstract  
Synthetic Velocity Mapping Cardiac MRI Coupled with Automated Left Ventricle Segmentation. Xing, X.; Wu, Y.; Firmin, D.; Gatehouse, P.; and Yang, G. In SPIE Medical Imaging 2022, pages 103, 2022.
Synthetic Velocity Mapping Cardiac MRI Coupled with Automated Left Ventricle Segmentation [pdf]Paper   doi   link   bibtex   abstract  
Amelioration of Alzheimer’s disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow. Xie, C.; Zhuang, X.; Niu, Z.; Ai, R.; Lautrup, S.; Zheng, S.; Jiang, Y.; Han, R.; Sen Gupta, T.; and Cao, S. Nature Biomedical Engineering. 2022.
Amelioration of Alzheimer’s disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow [pdf]Paper   link   bibtex  
Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers. Huang, J.; Fang, Y.; Nan, Y.; Wu, H.; Wu, Y.; Gao, Z.; Li, Y.; Wang, Z.; Lio, P.; Rueckert, D.; Eldar, Y., C.; and Yang, G. , (August). 2022.
Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers [pdf]Paper   Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers [link]Website   link   bibtex   abstract  
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Association between Left Ventricular Global Function Index and Outcomes in Patients with Dilated Cardiomyopathy. Liu, T.; Zhou, Z.; Bo, K.; Gao, Y.; Wang, H.; Wang, R.; Liu, W.; Chang, S.; Liu, Y.; and Sun, Y. Frontiers in Cardiovascular Medicine. 2021.
Association between Left Ventricular Global Function Index and Outcomes in Patients with Dilated Cardiomyopathy [pdf]Paper   link   bibtex  
A Comparative Study of Radiomics and Deep-learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT Images. Astaraki, M.; Yang, G.; Zakko, Y.; Toma-Dasu, L.; Smedby, Ö.; and Wang, C. Frontiers in Oncology. 2021.
A Comparative Study of Radiomics and Deep-learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT Images [pdf]Paper   link   bibtex  
Multi-Channel U-Net (MCUNet) Based Fast and Automated Segmentation for the 3-Directional Multislice Cine Myocardial Velocity Mapping. Wu, Y.; Hatipoglu, S.; Alonso-Álvarez, D.; Gatehouse, P.; Firmin, D.; Keegan, J.; and Yang, G. In Society for Cardiovascular Magnetic Resonance (SCMR) 24th Annual Meeting, 2021.
Multi-Channel U-Net (MCUNet) Based Fast and Automated Segmentation for the 3-Directional Multislice Cine Myocardial Velocity Mapping [pdf]Paper   link   bibtex  
ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation. Zhang, W.; Yang, G.; Huang, H.; Yang, W.; Xu, X.; Liu, Y.; and Lai, X. International Journal of Imaging Systems and Technology. 2021.
ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation [pdf]Paper   link   bibtex  
Atrial Scar Segmentation from 3D Late Gadolinium Enhanced Datasets: Effect of Time After Contrast Injection. Yang, G.; Chen, J.; Zhang, H.; Wage, R.; Allen, J.; Wong, T.; Mohiaddin, R.; Firmin, D.; and Keegan, J. In Society for Cardiovascular Magnetic Resonance (SCMR) 24th Annual Meeting, 2021.
Atrial Scar Segmentation from 3D Late Gadolinium Enhanced Datasets: Effect of Time After Contrast Injection [pdf]Paper   link   bibtex   5 downloads  
Fast and Automated Segmentation for the Three-Directional Multi-slice Cine Myocardial Velocity Mapping. Wu, Y.; Hatipoglu, S.; Alonso-Álvarez, D.; Gatehouse, P.; Li, B.; Gao, Y.; Firmin, D.; Keegan, J.; and Yang, G. Diagnostics. 2021.
Fast and Automated Segmentation for the Three-Directional Multi-slice Cine Myocardial Velocity Mapping [pdf]Paper   link   bibtex   6 downloads  
Transfer Learning Enhanced Generative Adversarial Networks for Multi-Channel MRI Reconstruction. Lv, J.; Li, G.; Tong, X.; Chen, W.; Huang, J.; Wang, C.; and Yang, G. Computers in Biology and Medicine. 2021.
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FIRE: Unsupervised Bi-directional Inter- and Intra-modality Registration Using Deep Networks. Wang, C.; Yang, G.; and Papanastasiou, G. In IEEE International Symposium on Computer-Based Medical Systems (CBMS 2021), 2021.
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Recent Advances in Fibrosis and Scar Segmentation from Cardiac MRI: A State-of-the-Art Review and Future Perspectives. Wu, Y.; Tang, Z.; Li, B.; Firmin, D.; and Yang, G. Frontiers in Physiology. 2021.
Recent Advances in Fibrosis and Scar Segmentation from Cardiac MRI: A State-of-the-Art Review and Future Perspectives [pdf]Paper   link   bibtex  
Temporal Cue Guided Video Highlight Detection with Low-Rank Audio-Visual Fusion. Ye, Q.; Shen, X.; Gao, Y.; Wang, Z.; Bi, Q.; Li, P.; and Yang, G. In International Conference on Computer Vision (ICCV 2021), 2021.
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MIASSR: An Approach for Medical Image Arbitrary Scale Super-Resolution. Zhu, J.; Tan, C.; Yang, J.; Yang*(Co-last), G.; and Lio*(Co-last), P. arXiv preprint arXiv:2105.10738. 2021.
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Textured-Based Deep Learning in Prostate Cancer Classification with 3T Multiparametric MRI: Comparison with PI-RADS-Based Classification. Liu, Y.; Zheng, H.; Liang, Z.; Miao, Q.; Brisbane, W.; Marks, L.; Raman, S.; Reiter, R.; Yang, G.; and Sung, K. Diagnostics. 2021.
Textured-Based Deep Learning in Prostate Cancer Classification with 3T Multiparametric MRI: Comparison with PI-RADS-Based Classification [pdf]Paper   link   bibtex  
Annealing Genetic GAN for Imbalanced Web Data Learning. Hao, J.; Wang, C.; Yang, G.; Gao, Z.; Zhang, J.; and Zhang, H. IEEE Transactions on Multimedia. 2021.
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Ageing and Alzheimer’s Disease: Application of Artificial Intelligence in Mechanistic Studies, Diagnosis, and Drug Development. Ai, R.; Jin, X.; Tang, B.; Yang, G.; Niu, Z.; and Fang, E., F. Artificial Intelligence in Medicine. 2021.
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Deep Learning Enables Prostate MRI Segmentation: A Large Cohort Evaluation with Inter-rater Variability Analysis. Liu, Y.; Miao, Q.; Surawech, C.; Zheng, H.; Nguyen, D.; Yang, G.; Raman, S.; and Sung, K. Frontiers in Oncology. 2021.
Deep Learning Enables Prostate MRI Segmentation: A Large Cohort Evaluation with Inter-rater Variability Analysis [pdf]Paper   link   bibtex  
Global and regional reproducibility of phasic left ventricular myocardial velocities obtained by three-directional cine myocardial velocity mapping. Hatipoglu, S.; Keegan, J.; Alonso-Álvarez, D.; Wu, Y.; Yang, G.; Wage, R.; Firmin, D.; and Gatehouse, P. In Society for Cardiovascular Magnetic Resonance (SCMR) 24th Annual Meeting, 2021.
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High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss. Li, G.; Lv, J.; Tong, X.; Wang, C.; and Yang, G. IEEE Access. 2021.
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Arbitrary Scale Super-Resolution for Medical Images. Zhu, J.; Tan, C.; Yang, J.; Yang, G.; and Lio’, P. International journal of neural systems, 31(10): 2150037. 2021.
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Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data. Chen, J.; Zhang, H.; Mohaiddin, R.; Wong, T.; Firmin, D.; Keegan, J.; and Yang, G. IEEE Transactions on Medical Imaging. 2021.
Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data [pdf]Paper   link   bibtex  
Which GAN? A comparative study of generative adversarial network-based fast MRI reconstruction. Lv, J.; Zhu, J.; and Yang, G. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2200). 2021.
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Wavelet improved GAN for MRI reconstruction. Chen, Y.; Firmin, D.; and Yang, G. 2021.
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Fully automatic framework for comprehensive coronary artery calcium scores analysis on non-contrast cardiac-gated CT scan: Total and vessel-specific quantifications. Zhang, N.; Yang, G.; Zhang, W.; Wang, W.; Zhou, Z.; Zhang, H.; Xu, L.; and Chen, Y. European Journal of Radiology, 134(September 2020): 109420. 2021.
Fully automatic framework for comprehensive coronary artery calcium scores analysis on non-contrast cardiac-gated CT scan: Total and vessel-specific quantifications [pdf]Paper   Fully automatic framework for comprehensive coronary artery calcium scores analysis on non-contrast cardiac-gated CT scan: Total and vessel-specific quantifications [link]Website   doi   link   bibtex   abstract  
FA-GAN: Fused attentive generative adversarial networks for MRI image super-resolution. Jiang, M.; Zhi, M.; Wei, L.; Yang, X.; Zhang, J.; Li, Y.; Wang, P.; Huang, J.; and Yang, G. Computerized Medical Imaging and Graphics, 92(July): 101969. 2021.
FA-GAN: Fused attentive generative adversarial networks for MRI image super-resolution [pdf]Paper   FA-GAN: Fused attentive generative adversarial networks for MRI image super-resolution [link]Website   doi   link   bibtex   abstract  
3D PBV-Net: An automated prostate MRI data segmentation method. Jin, Y.; Yang, G.; Fang, Y.; Li, R.; Xu, X.; Liu, Y.; and Lai, X. Computers in Biology and Medicine, 128(November 2020): 104160. 2021.
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Multi-task learning with Multi-view Weighted Fusion Attention for artery-specific calcification analysis. Zhang, W.; Yang, G.; Zhang, N.; Xu, L.; Wang, X.; Zhang, Y.; Zhang, H.; Del Ser, J.; and de Albuquerque, V., H., C. Information Fusion, 71(February): 64-76. 2021.
Multi-task learning with Multi-view Weighted Fusion Attention for artery-specific calcification analysis [pdf]Paper   Multi-task learning with Multi-view Weighted Fusion Attention for artery-specific calcification analysis [link]Website   doi   link   bibtex   abstract  
Myocardial extracellular volume fraction quantification in an animal model of the doxorubicin-induced myocardial fibrosis: A synthetic hematocrit method using 3T cardiac magnetic resonance. Zhou, Z.; Wang, R.; Wang, H.; Liu, Y.; Lu, D.; Sun, Z.; Yang, G.; and Xu, L. Quantitative Imaging in Medicine and Surgery, 11(2): 510-520. 2021.
Myocardial extracellular volume fraction quantification in an animal model of the doxorubicin-induced myocardial fibrosis: A synthetic hematocrit method using 3T cardiac magnetic resonance [pdf]Paper   doi   link   bibtex   abstract  
DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis. Wang, C.; Yang, G.; Papanastasiou, G.; Tsaftaris, S., A.; Newby, D., E.; Gray, C.; Macnaught, G.; and MacGillivray, T., J. Information Fusion, 67(May 2020): 147-160. 2021.
DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis [pdf]Paper   DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis [link]Website   doi   link   bibtex   abstract  
A 2-year investigation of the impact of the computed tomography–derived fractional flow reserve calculated using a deep learning algorithm on routine decision-making for coronary artery disease management. Liu, X.; Mo, X.; Zhang, H.; Yang, G.; Shi, C.; and Hau, W., K. European Radiology, 31(9): 7039-7046. 2021.
A 2-year investigation of the impact of the computed tomography–derived fractional flow reserve calculated using a deep learning algorithm on routine decision-making for coronary artery disease management [pdf]Paper   doi   link   bibtex   abstract  
Multitask Learning for Estimating Multitype Cardiac Indices in MRI and CT Based on Adversarial Reverse Mapping. Yu, C.; Gao, Z.; Zhang, W.; Yang, G.; Zhao, S.; Zhang, H.; Zhang, Y.; and Li, S. IEEE Transactions on Neural Networks and Learning Systems, 32(2): 493-506. 2021.
Multitask Learning for Estimating Multitype Cardiac Indices in MRI and CT Based on Adversarial Reverse Mapping [pdf]Paper   doi   link   bibtex   abstract  
Automated multi-channel segmentation for the 4D myocardial velocity mapping cardiac MR. Wu, Y.; Hatipoglu, S.; Alonso-Álvarez, D.; Gatehouse, P.; Firmin, D.; Keegan, J.; and Yang, G. In Drukker, K.; and Mazurowski, M., A., editor(s), Medical Imaging 2021: Computer-Aided Diagnosis, pages 22, 2 2021. SPIE
Automated multi-channel segmentation for the 4D myocardial velocity mapping cardiac MR [pdf]Paper   Automated multi-channel segmentation for the 4D myocardial velocity mapping cardiac MR [link]Website   doi   link   bibtex   abstract  
Texture-Based Deep Learning for Prostate Cancer Classification with Multiparametric MRI. Liu, Y.; Zheng, H.; Liang, Z.; Qi, M.; Brisbane, W.; Marks, L.; Raman, S.; Reiter, R.; Yang, G.; and Sung, K. In International Society for Magnetic Resonance in Medicine, pages 1-3, 2021.
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Industrial Cyber-Physical Systems-Based Cloud IoT Edge for Federated Heterogeneous Distillation. Wang, C.; Yang, G.; Papanastasiou, G.; Zhang, H.; Rodrigues, J., J.; and De Albuquerque, V., H., C. IEEE Transactions on Industrial Informatics, 17(8): 5511-5521. 2021.
Industrial Cyber-Physical Systems-Based Cloud IoT Edge for Federated Heterogeneous Distillation [pdf]Paper   doi   link   bibtex   abstract  
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Roberts, M.; Driggs, D.; Thorpe, M.; Gilbey, J.; Yeung, M.; Ursprung, S.; Aviles-Rivero, A., I.; Etmann, C.; McCague, C.; Beer, L.; Weir-McCall, J., R.; Teng, Z.; Gkrania-Klotsas, E.; Ruggiero, A.; Korhonen, A.; Jefferson, E.; Ako, E.; Langs, G.; Gozaliasl, G.; Yang, G.; Prosch, H.; Preller, J.; Stanczuk, J.; Tang, J.; Hofmanninger, J.; Babar, J.; Sánchez, L., E.; Thillai, M.; Gonzalez, P., M.; Teare, P.; Zhu, X.; Patel, M.; Cafolla, C.; Azadbakht, H.; Jacob, J.; Lowe, J.; Zhang, K.; Bradley, K.; Wassin, M.; Holzer, M.; Ji, K.; Ortet, M., D.; Ai, T.; Walton, N.; Lio, P.; Stranks, S.; Shadbahr, T.; Lin, W.; Zha, Y.; Niu, Z.; Rudd, J., H.; Sala, E.; and Schönlieb, C., B. Nature Machine Intelligence, 3(3): 199-217. 2021.
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans [pdf]Paper   doi   link   bibtex   abstract  
Multiparameter Synchronous Measurement with IVUS Images for Intelligently Diagnosing Coronary Cardiac Disease. Cao, Y.; Wang, Z.; Liu, Z.; Li, Y.; Xiao, X.; Sun, L.; Zhang, Y.; Hou, H.; Zhang, P.; and Yang, G. IEEE Transactions on Instrumentation and Measurement, 70: 1-10. 2021.
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A Deep Multi-Task Learning Framework for Brain Tumor Segmentation. Huang, H.; Yang, G.; Zhang, W.; Xu, X.; Yang, W.; Jiang, W.; and Lai, X. Frontiers in Oncology, 11(June): 1-16. 2021.
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Explainable AI for COVID-19 CT Classifiers: An initial comparison study. Ye, Q.; Xia, J.; and Yang, G. Proceedings - IEEE Symposium on Computer-Based Medical Systems, 2021-June: 521-526. 2021.
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Can Clinical Symptoms and Laboratory Results Predict CT Abnormality? Initial Findings Using Novel Machine Learning Techniques in Children With COVID-19 Infections. Ma, H.; Ye, Q.; Ding, W.; Jiang, Y.; Wang, M.; Niu, Z.; Zhou, X.; Gao, Y.; Wang, C.; Menpes-Smith, W.; Fang, E., F.; Shao, J.; Xia, J.; and Yang, G. Frontiers in Medicine, 8(June): 1-10. 2021.
Can Clinical Symptoms and Laboratory Results Predict CT Abnormality? Initial Findings Using Novel Machine Learning Techniques in Children With COVID-19 Infections [pdf]Paper   doi   link   bibtex   abstract  
Machine learning for covid-19 diagnosis and prognostication: Lessons for amplifying the signal while reducing the noise. Driggs, D.; Selby, I.; Roberts, M.; Gkrania-Klotsas, E.; Rudd, J., H.; Yang, G.; Babar, J.; Sala, E.; and Schönlieb, C., B. Radiology: Artificial Intelligence, 3(4). 2021.
Machine learning for covid-19 diagnosis and prognostication: Lessons for amplifying the signal while reducing the noise [pdf]Paper   doi   link   bibtex  
Recent advances in artificial intelligence for cardiac imaging. Yang, G.; Zhang, H.; Firmin, D.; and Li, S. Computerized Medical Imaging and Graphics, 90. 2021.
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Three-Dimensional Embedded Attentive RNN (3D-EAR) Segmentor for Left Ventricle Delineation from Myocardial Velocity Mapping. Kuang, M.; Wu, Y.; Alonso-Álvarez, D.; Firmin, D.; Keegan, J.; Gatehouse, P.; and Yang, G. Volume 12738 . Functional Imaging and Modeling of the Heart. FIMH 2021. Lecture Notes in Computer Science, pages 55-62. 2021.
Functional Imaging and Modeling of the Heart. FIMH 2021. Lecture Notes in Computer Science [pdf]Paper   Functional Imaging and Modeling of the Heart. FIMH 2021. Lecture Notes in Computer Science [link]Website   doi   link   bibtex  
Association between right ventricular strain and outcomes in patients with dilated cardiomyopathy. Liu, T.; Gao, Y.; Wang, H.; Zhou, Z.; Wang, R.; Chang, S., S.; Liu, Y.; Sun, Y.; Rui, H.; Yang, G.; Firmin, D.; Dong, J.; and Xu, L. Heart, 107(15): 1233-1239. 2021.
Association between right ventricular strain and outcomes in patients with dilated cardiomyopathy [pdf]Paper   doi   link   bibtex   abstract  
A mathematical model for predicting intracranial pressure based on noninvasively acquired PC-MRI parameters in communicating hydrocephalus. Long, J.; Sun, D.; Zhou, X.; Huang, X.; Hu, J.; Xia, J.; and Yang, G. Journal of Clinical Monitoring and Computing, 35(6): 1325-1332. 2021.
A mathematical model for predicting intracranial pressure based on noninvasively acquired PC-MRI parameters in communicating hydrocephalus [pdf]Paper   A mathematical model for predicting intracranial pressure based on noninvasively acquired PC-MRI parameters in communicating hydrocephalus [link]Website   doi   link   bibtex   abstract  
Physiologically personalized coronary blood flow model to improve the estimation of non-invasive fractional flow reserve. Liu, X.; Xu, C.; Rao, S.; Zhang, Y.; Ghista, D.; Gao, Z.; and Yang, G. Medical Physics, 49(1): 583-597. 2021.
Physiologically personalized coronary blood flow model to improve the estimation of non-invasive fractional flow reserve [pdf]Paper   doi   link   bibtex   abstract  
Incorporating Boundary Uncertainty into Loss Functions for Biomedical Image Segmentation. Yeung, M.; Yang, G.; Sala, E.; Schönlieb, C.; and Rundo, L. arXiv preprint arXiv:2111.00533. 2021.
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A Parallel Imaging Coupled Generative Adversarial Network for Accelerated Multi-Channel MRI Reconstruction. Lv, J.; Wang, C.; and Yang, G. In International Society for Magnetic Resonance in Medicine, 2021.
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In-vivo cardiac diffusion weighted image registration aided by AI semantic segmentation. Ferreira, P.; Martı́n, R.; Khalique, Z.; Scott, A.; Yang, G.; Nielles-Vallespin, S.; Pennell, D.; and Firmin, D. In International Society for Magnetic Resonance in Medicine, 2020.
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Automating the ABCD Rule for Melanoma Detection: A Survey. Ali, A.; Li, J.; and Yang, G. IEEE Access,83333-83346. 2020.
Automating the ABCD Rule for Melanoma Detection: A Survey [pdf]Paper   link   bibtex  
A Novel Fuzzy Multilayer Perceptron (F-MLP) for the Detection of Irregularity in Skin Lesion Border Using Dermoscopic Images. Ali, A.; Li, J.; Kanwal, S.; Yang, G.; Hussain, A.; and Jane O'shea, S. Frontiers in Medicine. 2020.
A Novel Fuzzy Multilayer Perceptron (F-MLP) for the Detection of Irregularity in Skin Lesion Border Using Dermoscopic Images [pdf]Paper   link   bibtex  
Systematic and Comprehensive Automated Ventricle Segmentation on Ventricle Images of the Elderly Patients: A Retrospective Study. Zhou, X.; Ye, Q.; Jiang, Y.; Wang, M.; Niu, Z.; Menpes-Smith, W.; Fang, E., F.; Liu, Z.; Xia, J.; and Yang, G. Frontiers in Aging Neuroscience. 2020.
Systematic and Comprehensive Automated Ventricle Segmentation on Ventricle Images of the Elderly Patients: A Retrospective Study [pdf]Paper   link   bibtex  
Deep RetinaNet for Dynamic Left Ventricle Detection in Multiview Echocardiography Classification. Yang, M.; Xiao, X.; Liu, Z.; Sun, L.; Guo, W.; Cui, L.; Sun, D.; Zhang, P.; and Yang, G. Scientific Programming. 2020.
Deep RetinaNet for Dynamic Left Ventricle Detection in Multiview Echocardiography Classification [pdf]Paper   link   bibtex  
SARA-GAN: Self-Attention and Relative Average Discriminator Based Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction. Yuan, Z.; Jiang, M.; Wang, Y.; Wei, B.; Li, Y.; Wang, P.; Menpes-Smith, W.; Niu, Z.; and Yang, G. Frontiers in Neuroinformatics. 2020.
SARA-GAN: Self-Attention and Relative Average Discriminator Based Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction [pdf]Paper   link   bibtex  
Comparison Study of Radiomics and Deep Learning Based Methods for Thyroid Nodules Classification Using Ultrasound Images. Wang, Y.; Yue, W.; Li, X.; Liu, S.; Guo, L.; Xu, H.; Zhang, H.; and Yang, G. IEEE Access,52010-52017. 2020.
Comparison Study of Radiomics and Deep Learning Based Methods for Thyroid Nodules Classification Using Ultrasound Images [pdf]Paper   link   bibtex  
Multimodal MRI to aid prediction of low-grade glioma growth characteristics. Howe, F.; Jones, T.; Rich, P.; Colman, J.; Yang, G.; Raschke, F.; Liang, V.; Denley, A.; and Barrick, T. In International Society for Magnetic Resonance in Medicine, 2020.
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MV-RAN: Multiview Recurrent Aggregation Network for Echocardiographic Sequences Segmentation and Full Cardiac Cycle Analysis. Li, M.; Wang, C.; Zhang, H.; and Yang, G. Computers in Biology and Medicine,103728. 2020.
MV-RAN: Multiview Recurrent Aggregation Network for Echocardiographic Sequences Segmentation and Full Cardiac Cycle Analysis [pdf]Paper   link   bibtex  
Exploring Uncertainty Measures in Bayesian Deep Attentive Neural Networks for Prostate Zonal Segmentation. Liu, Y.; Yang, G.; Hosseiny, M.; Azadikhah, A.; Afshari Mirak, S.; Miao, Q.; Raman, S.; and Sung, K. IEEE Access,151817-151828. 2020.
Exploring Uncertainty Measures in Bayesian Deep Attentive Neural Networks for Prostate Zonal Segmentation [pdf]Paper   link   bibtex  
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness. Guo, Y.; Wang, C.; Zhang, H.; and Yang, G. In Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), 2020.
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness [pdf]Paper   link   bibtex  
Idiopathic Normal Pressure Hydrocephalus and Elderly Acquired Hydrocephalus: Evaluation with Cerebrospinal Fluid Flow and Ventricular Volume Parameters. He, W.; Zhou, X.; Long, J.; Xu, Q.; Huang, X.; Jiang, J.; Xia, J.; and Yang, G. Frontiers in Aging Neuroscience. 2020.
Idiopathic Normal Pressure Hydrocephalus and Elderly Acquired Hydrocephalus: Evaluation with Cerebrospinal Fluid Flow and Ventricular Volume Parameters [pdf]Paper   link   bibtex  
Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images. Hu, S.; Gao, Y.; Niu, Z.; Jiang, Y.; Li, L.; Xiao, X.; Wang, M.; Fang, E., F.; Menpes-Smith, W.; Xia, J.; and others IEEE Access,118869-118883. 2020.
Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images [pdf]Paper   link   bibtex  
Machine learning for COVID-19 detection and prognostication using chest radiographs and CT scans: a systematic methodological review. Roberts, M.; Driggs, D.; Thorpe, M.; Gilbey, J.; Yeung, M.; Ursprung, S.; Aviles-Rivero, A., I.; Etmann, C.; McCague, C.; Beer, L.; and others arXiv preprint arXiv:2008.06388. 2020.
Machine learning for COVID-19 detection and prognostication using chest radiographs and CT scans: a systematic methodological review [pdf]Paper   link   bibtex  
Simultaneous Left Atrium Anatomy and Scar Segmentations via Deep Learning in Multiview Information with Attention. Yang, G.; Chen, J.; Gao, Z.; Li, S.; Ni, H.; Angelini, E.; Wong, T.; Mohiaddin, R.; Nyktari, E.; Wage, R.; and others Future Generation Computer Systems. 2020.
Simultaneous Left Atrium Anatomy and Scar Segmentations via Deep Learning in Multiview Information with Attention [pdf]Paper   link   bibtex  
Direct Quantification of Coronary Artery Stenosis through Hierarchical Attentive Multi-view Learning. Zhang, D.; Yang, G.; Zhao, S.; Zhang, Y.; Zhang, H.; Ghista, D.; and Li, S. IEEE Transactions on Medical Imaging. 2020.
Direct Quantification of Coronary Artery Stenosis through Hierarchical Attentive Multi-view Learning [pdf]Paper   link   bibtex  
SaliencyGAN: Deep Learning Semisupervised Salient Object Detection in the Fog of IoT. Wang, C.; Dong, S.; Zhao, X.; Papanastasiou, G.; Zhang, H.; and Yang, G. IEEE Transactions on Industrial Informatics, 16(4): 2667-2676. 2020.
SaliencyGAN: Deep Learning Semisupervised Salient Object Detection in the Fog of IoT [pdf]Paper   doi   link   bibtex   abstract  
Annealing Genetic GAN for Minority Oversampling. Hao, J.; Wang, C.; Zhang, H.; and Yang, G. 31st British Machine Vision Conference, BMVC 2020,1-12. 2020.
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Automating in vivo cardiac diffusion tensor postprocessing with deep learning–based segmentation. Ferreira, P., F.; Martin, R., R.; Scott, A., D.; Khalique, Z.; Yang, G.; Nielles-Vallespin, S.; Pennell, D., J.; and Firmin, D., N. Magnetic Resonance in Medicine, 84(5): 2801-2814. 2020.
Automating in vivo cardiac diffusion tensor postprocessing with deep learning–based segmentation [pdf]Paper   doi   link   bibtex   abstract  
A research agenda for ageing in China in the 21st century (2nd edition): Focusing on basic and translational research, long-term care, policy and social networks. Fang, E., F.; Xie, C.; Schenkel, J., A.; Wu, C.; Long, Q.; Cui, H.; Aman, Y.; Frank, J.; Liao, J.; Zou, H.; Wang, N., Y.; Wu, J.; Liu, X.; Li, T.; Fang, Y.; Niu, Z.; Yang, G.; Hong, J.; Wang, Q.; Chen, G.; Li, J.; Chen, H., Z.; Kang, L.; Su, H.; Gilmour, B., C.; Zhu, X.; Jiang, H.; He, N.; Tao, J.; Leng, S., X.; Tong, T.; and Woo, J. Ageing Research Reviews, 64(September). 2020.
A research agenda for ageing in China in the 21st century (2nd edition): Focusing on basic and translational research, long-term care, policy and social networks [pdf]Paper   doi   link   bibtex   abstract  
Atrial scar quantification via multi-scale CNN in the graph-cuts framework. Li, L.; Wu, F.; Yang, G.; Xu, L.; Wong, T.; Mohiaddin, R.; Firmin, D.; Keegan, J.; and Zhuang, X. Medical Image Analysis, 60. 2020.
Atrial scar quantification via multi-scale CNN in the graph-cuts framework [pdf]Paper   doi   link   bibtex   abstract  
The NAD+-mitophagy axis in healthy longevity and in artificial intelligence-based clinical applications. Aman, Y.; Frank, J.; Lautrup, S., H.; Matysek, A.; Niu, Z.; Yang, G.; Shi, L.; Bergersen, L., H.; Storm-Mathisen, J.; Rasmussen, L., J.; Bohr, V., A.; Nilsen, H.; Fang, E., F.; and others Mechanisms of Ageing and Development, 185(November 2019): 111194. 2020.
The NAD+-mitophagy axis in healthy longevity and in artificial intelligence-based clinical applications [pdf]Paper   The NAD+-mitophagy axis in healthy longevity and in artificial intelligence-based clinical applications [link]Website   doi   link   bibtex   abstract  
Salient Object Detection in the Distributed Cloud-Edge Intelligent Network. Gao, Z.; Dong, S.; Sun, S.; Wang, X.; Yang, G.; Wu, W.; Li, S.; Zhang, H.; and Albuquerque, V., H., C. IEEE Network. 2020.
Salient Object Detection in the Distributed Cloud-Edge Intelligent Network [pdf]Paper   link   bibtex  
A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images. Ali, A.; Li, J.; Yang, G.; and O’Shea, S., J. PeerJ Computer Science. 2020.
A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images [pdf]Paper   link   bibtex  
Catheter ablation vs. thoracoscopic surgical ablation in long-standing persistent atrial fibrillation: CASA-AF randomized controlled trial. Haldar, S.; Khan, H., R.; Boyalla, V.; Kralj-Hans, I.; Jones, S.; Lord, J.; Onyimadu, O.; Satishkumar, A.; Bahrami, T.; De Souza, A.; and others European Heart Journal. 2020.
Catheter ablation vs. thoracoscopic surgical ablation in long-standing persistent atrial fibrillation: CASA-AF randomized controlled trial [pdf]Paper   link   bibtex  
USR-Net: A Simple Unsupervised Single-Image Super-Resolution Method for Late Gadolinium Enhancement CMR. Zhu, J.; Yang, G.; Wong, T.; Mohiaddin, R.; Firmin, D.; Keegan, J.; and Lio, P. In International Society for Magnetic Resonance in Medicine, 2020.
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A ROI Focused Multi-Scale Super-Resolution Method for the Diffusion Tensor Cardiac Magnetic Resonance. Zhu, J.; Yang, G.; Ferreira, P.; Scott, A.; Nielles-Vallespin, S.; Keegan, J.; Pennell, D.; Lio, P.; and Firmin, D. In International Society for Magnetic Resonance in Medicine, 2019.
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A Deep Learning Based Approach to Skin Lesion Border Extraction With a Novel Edge Detector in Dermoscopy Images. Ali, A.; Li, J.; O'Shea, S., J.; Yang, G.; Trappenberg, T.; and Ye, X. In IEEE International Joint Conference on Neural Networks, 2019.
A Deep Learning Based Approach to Skin Lesion Border Extraction With a Novel Edge Detector in Dermoscopy Images [pdf]Paper   link   bibtex  
Direct Quantification for Coronary Artery Stenosis Using Multiview Learning. Zhang, D.; Yang, G.; Zhao, S.; Zhang, Y.; Zhang, H.; and Li, S. In Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), 2019.
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Automatic Prostate Zonal Segmentation Using Fully Convolutional Network with Feature Pyramid Attention. Liu, Y.; Yang, G.; Mirak, S., A.; Hosseiny, M.; Azadikhah, A.; Zhong, X.; Reiter, R.; Lee, Y.; Raman, S.; and Sung, K. IEEE Access,163626-163632. 2019.
Automatic Prostate Zonal Segmentation Using Fully Convolutional Network with Feature Pyramid Attention [pdf]Paper   link   bibtex  
Discriminative Consistent Domain Generation for Semi-supervised Learning. Chen, J.; Zhang, H.; Zhang, Y.; Zhao, S.; Mohiaddin, R.; Wong, T.; Firmin, D.; Yang, G.; and Keegan, J. In Medical Image Computing and Computer Assisted Intervention (MICCAI 2019), 2019.
Discriminative Consistent Domain Generation for Semi-supervised Learning [pdf]Paper   link   bibtex  
Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge. Zhuang, X.; Li, L.; Payer, C.; Stern, D.; Urschler, M.; Heinrich, M., P.; Oster, J.; Wang, C.; Smedby, O.; Bian, C.; and others Medical Image Analysis. 2019.
Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge [pdf]Paper   link   bibtex  
A Deep Learning Based Left Atrium Anatomy Segmentation and Scar Delineation in 3D Late Gadolinium Enhanced CMR Images. Yang, G.; Chen, J.; Gao, Z.; Ni, H.; Angelini, E.; Wong, T.; Mohiaddin, R.; Nyktari, E.; Wage, R.; Xu, L.; and others In International Society for Magnetic Resonance in Medicine, 2019.
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A Two-Stage U-Net Model for 3D Multi-class Segmentation on Full-Resolution Cardiac Data. Wang, C.; MacGillivray, T.; Macnaught, G.; Yang, G.; and Newby, D. In International Workshop on Statistical Atlases and Computational Models of the Heart, pages 191-199, 2019. Springer
A Two-Stage U-Net Model for 3D Multi-class Segmentation on Full-Resolution Cardiac Data [pdf]Paper   link   bibtex  
Tissue-type mapping of gliomas. Raschke, F.; Barrick, T., R.; Jones, T., L.; Yang, G.; Ye, X.; and Howe, F., A. NeuroImage: Clinical, 21(July 2018): 101648. 2019.
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Automatic skin lesion segmentation by coupling deep fully convolutional networks and shallow network with textons. Zhang, L.; Yang, G.; and Ye, X. Journal of Medical Imaging, 6(02): 1. 2019.
Automatic skin lesion segmentation by coupling deep fully convolutional networks and shallow network with textons [pdf]Paper   doi   link   bibtex   abstract  
Lesion focused super-resolution. Zhu, J.; Yang, G.; and Lio, P. In SPIE Medical Imaging 2019, pages 56, 2019.
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How Can We Make Gan Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale Approach. Zhu, J.; Yang, G.; and Lio, P. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pages 1669-1673, 4 2019. IEEE
How Can We Make Gan Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale Approach [pdf]Paper   How Can We Make Gan Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale Approach [link]Website   doi   link   bibtex  
TPSDicyc: Improved Deformation Invariant Cross-domain Medical Image Synthesis. Wang, C.; Papanastasiou, G.; Tsaftaris, S.; Yang, G.; Gray, C.; Newby, D.; Macnaught, G.; and MacGillivray, T. Volume 11905 LNCS Springer International Publishing, 2019.
TPSDicyc: Improved Deformation Invariant Cross-domain Medical Image Synthesis [pdf]Paper   TPSDicyc: Improved Deformation Invariant Cross-domain Medical Image Synthesis [link]Website   doi   link   bibtex   abstract  
3D U2-Net: A 3D Universal U-Net for Multi-domain Medical Image Segmentation. Huang, C.; Han, H.; Yao, Q.; Zhu, S.; and Zhou, K., S. Volume 1 Springer International Publishing, 2019.
3D U2-Net: A 3D Universal U-Net for Multi-domain Medical Image Segmentation [pdf]Paper   3D U2-Net: A 3D Universal U-Net for Multi-domain Medical Image Segmentation [link]Website   doi   link   bibtex   abstract  
Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac cine MRI. Zhang, N.; Yang, G.; Gao, Z.; Xu, C.; Zhang, Y.; Shi, R.; Keegan, J.; Xu, L.; Zhang, H.; Fan, Z.; and Firmin, D. Radiology, 291(3): 606-607. 2019.
Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac cine MRI [pdf]Paper   doi   link   bibtex   abstract  
A Two-Stage U-Net Model for 3D Multi-class Segmentation on Full-Resolution Cardiac Data. Wang, C.; MacGillivray, T.; Macnaught, G.; Yang, G.; and Newby, D. International Workshop on Statistical Atlases and Computational Models of the Heart, pages 191-199. Springer, Cham, 2019.
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Atrial scar segmentation via potential learning in the graph-cut framework. Li, L.; Yang, G.; Wu, F.; Wong, T.; Mohiaddin, R.; Firmin, D.; Keegan, J.; Xu, L.; and Zhuang, X. In International Workshop on Statistical Atlases and Computational Models of the Heart, pages 152-160, 2019. Springer, Cham
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A Single-Image Super-Resolution Method for Late Gadolinium Enhancement CMR. Zhu, J.; Yang, G.; Wong, T.; Mohiaddin, R.; Firmin, D.; Keegan, J.; and Lio, P. In International Society for Magnetic Resonance in Medicine, 2019.
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Bayesian VoxDRN: A Probabilistic Deep Voxelwise Dilated Residual Network for Whole Heart Segmentation from 3D MR Images. Shi, Z.; Zeng, G.; Zhang, L.; Zhuang, X.; Li, L.; Yang, G.; and Zheng, G. In Medical Image Computing and Computer Assisted Intervention (MICCAI 2018), 2018.
Bayesian VoxDRN: A Probabilistic Deep Voxelwise Dilated Residual Network for Whole Heart Segmentation from 3D MR Images [pdf]Paper   link   bibtex  
Deep U-Net Reconstruction for Undersampled Spiral Diffusion Tensor Cardiovascular Magnetic Resonance. Luk YH, A.; Yang, G.; Ferreira, P.; Nielles-Vallespin, S.; Gorodezky, M.; Khalique, Z.; Pennell, D.; Firmin, D.; and Scott, A. In International Society for Magnetic Resonance in Medicine Workshop on Machine Learning, Part II, 2018.
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Automatic myocardial segmentation of cardiovascular diffusion tensor images with a convolutional neural network. Ferreira, P.; Khalique, Z.; Scott, A.; Yang, G.; Nielles-Vallespin, S.; Pennell, D.; and Firmin, D. In International Society for Magnetic Resonance in Medicine Workshop on Machine Learning, Part II, 2018.
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Deep Learning Using TensorLayer. Dong, H.; Guo, Y.; and Yang, G. ISBN 978-7-121-32622-6, Publishing House of Electronics Industry, 2018.
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Multiview Sequential Learning and Dilated Residual Learning for a Fully Automatic Delineation of the Left Atrium and Pulmonary Veins from Late Gadolinium-Enhanced Cardiac MRI Images. Yang, G.; Chen, J.; Gao, Z.; Zhang, H.; Ni, H.; Angelini, E.; Mohiaddin, R.; Wong, T.; Keegan, J.; and Firmin, D. In The 40th IEEE International Engineering in Medicine and Biology Conference, 2018.
Multiview Sequential Learning and Dilated Residual Learning for a Fully Automatic Delineation of the Left Atrium and Pulmonary Veins from Late Gadolinium-Enhanced Cardiac MRI Images [pdf]Paper   link   bibtex  
Holistic and Deep Feature Pyramids for Saliency Detection. Dong, S.; Gao, Z.; Sun, S.; Wang, X.; Li, M.; Zhang, H.; Yang, G.; Liu, H.; and Li, S. In British Machine Vision Conference, 2018.
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Review for "Guide to Medical Image Analysis: Methods and Algorithms". Yang, G. Technical Report 2018.
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Deep Learning intra-image and inter-images features for Co-saliency detection. Li, M.; Dong, S.; Zhang, K.; Gao, Z.; Wu, X.; Zhang, H.; Yang, G.; and Li, S. In British Machine Vision Conference, 2018.
Deep Learning intra-image and inter-images features for Co-saliency detection [pdf]Paper   link   bibtex  
Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI. Schlemper, J.; Yang, G.; Ferreira, P.; Scott, A.; McGill, L.; Khalique, Z.; Gorodezky, M.; Roehl, M.; Keegan, J.; Pennell, D.; and others In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 295-303, 2018. Springer
Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI [pdf]Paper   link   bibtex  
DAGAN: Deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction. Yang, G.; Yu, S.; Dong, H.; Slabaugh, G.; Dragotti, P., L.; Ye, X.; Liu, F.; Arridge, S.; Keegan, J.; Guo, Y.; and others IEEE Transactions on Medical Imaging, 37(6): 1310-1321. 2018.
DAGAN: Deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction [pdf]Paper   link   bibtex  
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction. Seitzer, M.; Yang, G.; Schlemper, J.; Oktay, O.; Wuerfl, T.; Christlein, V.; Wong, T.; Mohiaddin, R.; Firmin, D.; Keegan, J.; and others In Medical Image Computing and Computer Assisted Intervention (MICCAI 2018), 2018.
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction [pdf]Paper   link   bibtex  
Left Atrial Scarring Segmentation from Delayed-Enhancement Cardiac MRI Images: A Deep Learning Approach. Yang, G.; Zhuang, X.; Khan, H.; Nyktari, E.; Haldar, S.; Li, L.; Wage, R.; Ye, X.; Slabaugh, G.; Mohiaddin, R.; Wong, T.; Keegan, J.; and Firmin, D. Cardiovascular Imaging and Image Analysis,109-130. 2018.
Left Atrial Scarring Segmentation from Delayed-Enhancement Cardiac MRI Images: A Deep Learning Approach [pdf]Paper   doi   link   bibtex   abstract  
Multiview two-task recursive attention model for left atrium and atrial scars segmentation. Chen, J.; Yang, G.; Gao, Z.; Ni, H.; Angelini, E.; Mohiaddin, R.; Wong, T.; Zhang, Y.; Du, X.; Zhang, H.; Keegan, J.; and Firmin, D. Volume 11071 LNCS Springer International Publishing, 2018.
Multiview two-task recursive attention model for left atrium and atrial scars segmentation [pdf]Paper   Multiview two-task recursive attention model for left atrium and atrial scars segmentation [link]Website   doi   link   bibtex   abstract  
Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-linear and Deep Learning Models. Olliverre, N.; Yang, G.; Slabaugh, G.; Reyes-Aldasoro, C., C.; and Alonso, E. Volume 11037 LNCS . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pages 130-138. Springer International Publishing, 2018.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [pdf]Paper   Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [link]Website   doi   link   bibtex  
Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels. Soltaninejad, M.; Yang, G.; Lambrou, T.; Allinson, N.; Jones, T., L.; Barrick, T., R.; Howe, F., A.; and Ye, X. Computer Methods and Programs in Biomedicine, 157: 69-84. 2018.
Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels [pdf]Paper   Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels [link]Website   doi   link   bibtex   abstract  
Fully automatic segmentation and objective assessment of atrial scars for long-standing persistent atrial fibrillation patients using late gadolinium-enhanced MRI. Yang, G.; Zhuang, X.; Khan, H.; Haldar, S.; Nyktari, E.; Li, L.; Wage, R.; Ye, X.; Slabaugh, G.; Mohiaddin, R.; Wong, T.; Keegan, J.; and Firmin, D. Medical Physics, 45(4): 1562-1576. 2018.
Fully automatic segmentation and objective assessment of atrial scars for long-standing persistent atrial fibrillation patients using late gadolinium-enhanced MRI [pdf]Paper   doi   link   bibtex   abstract  
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge. 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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge [pdf]Paper   Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge [link]Website   link   bibtex   abstract  
MRI Brain Tumor Segmentation and Patient Survival Prediction Using Random Forests and Fully Convolutional Networks. Soltaninejad, M.; Zhang, L.; Lambrou, T.; Yang, G.; Allinson, N.; and Ye, X. In Medical Image Computing and Computer Assisted Intervention MICCAI 2017 Brainlesion Workshop, volume 10670 LNCS, pages 204-215, 2018. Springer, Cham
MRI Brain Tumor Segmentation and Patient Survival Prediction Using Random Forests and Fully Convolutional Networks [pdf]Paper   doi   link   bibtex   abstract  
Atrial Fibrosis Quantification Based on Maximum Likelihood Estimator of Multivariate Images. Wu, F.; Yang, G.; Li, L.; Xu, L.; Wong, T.; Mohiaddin, R.; Firmin, D.; Keegan, J.; Zhuang, X.; Yang, G.; Wong, T.; Mohiaddin, R.; Firmin, D.; Keegan, J.; Xu, L.; and Zhuang, X. In Medical Image Computing and Computer Assisted Intervention (MICCAI 2018), volume 11073 LNCS, pages 604-612, 2018. Springer International Publishing
Atrial Fibrosis Quantification Based on Maximum Likelihood Estimator of Multivariate Images [pdf]Paper   Atrial Fibrosis Quantification Based on Maximum Likelihood Estimator of Multivariate Images [link]Website   doi   link   bibtex   abstract  
Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges: 8th International Workshop, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, September 10-14, 2017, Revised Selected Papers. Pop, M.; Sermesant, M.; Jodoin, P.; Lalande, A.; Zhuang, X.; Yang, G.; Young, A.; and Bernard, O. Volume 10663 Springer, 2018.
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The Deep Poincaré Map: A Novel Approach for Left Ventricle Segmentation. Mo, Y.; Liu, F.; McIlwraith, D.; Yang, G.; Zhang, J.; He, T.; and Guo, Y. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11073 LNCS: 561-568. 2018.
The Deep Poincaré Map: A Novel Approach for Left Ventricle Segmentation [pdf]Paper   The Deep Poincaré Map: A Novel Approach for Left Ventricle Segmentation [link]Website   doi   link   bibtex   abstract  
  2017 (9)
Tissue type mapping of gliomas using multimodal MRI. Raschke, F.; Barrick, T., R.; Yang, G.; Jones, T., L.; Ye, X.; and Howe, F., A. In International Society for Magnetic Resonance in Medicine (ISMRM) 21st Annual Meeting, 2017. International Society for Magnetic Resonance in Medicine
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Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks. Dong, H.; Yang, G.; Liu, F.; Mo, Y.; and Guo, Y. In Annual Conference on Medical Image Understanding and Analysis, Communications in Computer and Information Science, 2017.
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks [pdf]Paper   link   bibtex  
Segmenting Atrial Fibrosis from Late Gadolinium-Enhanced Cardiac MRI by Deep-Learned Features with Stacked Sparse Auto-Encoders. Yang, G.; Zhuang, X.; Khan, H.; Haldar, S.; Nyktari, E.; Ye, X.; Slabaugh, G.; Wong, T.; Mohiaddin, R.; Keegan, J.; and others In Annual Conference on Medical Image Understanding and Analysis, Communications in Computer and Information Science, pages 195-206, 2017. Springer, Cham
Segmenting Atrial Fibrosis from Late Gadolinium-Enhanced Cardiac MRI by Deep-Learned Features with Stacked Sparse Auto-Encoders [pdf]Paper   link   bibtex  
Deep De-Aliasing for Fast Compressive Sensing MRI. Yu, S.; Dong, H.; Yang, G.; Slabaugh, G.; Dragotti, P., L.; Ye, X.; Liu, F.; Arridge, S.; Keegan, J.; Firmin, D.; and others arXiv preprint arXiv:1705.07137. 2017.
Deep De-Aliasing for Fast Compressive Sensing MRI [pdf]Paper   link   bibtex  
Differentiation of pre-ablation and post-ablation late gadolinium-enhanced cardiac MRI scans of longstanding persistent atrial fibrillation patients. Yang, G.; Zhuang, X.; Khan, H.; Haldar, S.; Nyktari, E.; Li, L.; Ye, X.; Slabaugh, G.; Wong, T.; Mohiaddin, R.; Keegan, J.; and Firmin, D. Medical Imaging 2017: Computer-Aided Diagnosis, 10134(March 2017): 101340O. 2017.
Differentiation of pre-ablation and post-ablation late gadolinium-enhanced cardiac MRI scans of longstanding persistent atrial fibrillation patients [pdf]Paper   doi   link   bibtex   abstract  
Pairwise mixture model for unmixing partial volume effect in multi-voxel MR spectroscopy of brain tumour patients. Olliverre, N.; Asad, M.; Yang, G.; Howe, F.; and Slabaugh, G. Medical Imaging 2017: Computer-Aided Diagnosis, 10134(March 2017): 101341R. 2017.
Pairwise mixture model for unmixing partial volume effect in multi-voxel MR spectroscopy of brain tumour patients [pdf]Paper   doi   link   bibtex   abstract  
Multi-atlas propagation based left atrium segmentation coupled with super-voxel based pulmonary veins delineation in late gadolinium-enhanced cardiac MRI. Yang, G.; Zhuang, X.; Khan, H.; Haldar, S.; Nyktari, E.; Li, L.; Ye, X.; Slabaugh, G.; Wong, T.; Mohiaddin, R.; Keegan, J.; and Firmin, D. Medical Imaging 2017: Image Processing, 10133(February 2017): 1013313. 2017.
Multi-atlas propagation based left atrium segmentation coupled with super-voxel based pulmonary veins delineation in late gadolinium-enhanced cardiac MRI [pdf]Paper   doi   link   bibtex   abstract  
A fully automatic deep learning method for atrial scarring segmentation from late gadolinium-enhanced MRI images. Yang, G.; Zhuang, X.; Khan, H.; Haldar, S.; Nyktari, E.; Ye, X.; Slabaugh, G.; Wong, T.; Mohiaddin, R.; Keegan, J.; and others In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI), pages 844-848, 2017. IEEE
A fully automatic deep learning method for atrial scarring segmentation from late gadolinium-enhanced MRI images [pdf]Paper   link   bibtex  
Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Soltaninejad, M.; Yang, G.; Lambrou, T.; Allinson, N.; Jones, T., L.; Barrick, T., R.; Howe, F., A.; and Ye, X. International journal of computer assisted radiology and surgery, 12(2): 183-203. 2017.
Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI [pdf]Paper   link   bibtex  
  2016 (6)
Supervised Partial Volume Effect Unmixing for Brain Tumor Characterization using Multi-voxel MR Spectroscopic Imaging. Asad, M.; Yang, G.; and Slabaugh, G. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pages 436-439, 2016. IEEE
Supervised Partial Volume Effect Unmixing for Brain Tumor Characterization using Multi-voxel MR Spectroscopic Imaging [pdf]Paper   link   bibtex  
Super-Resolved Enhancement of a Single Image and Its Application in Cardiac MRI. Yang, G.; Ye, X.; Slabaugh, G.; Keegan, J.; Mohiaddin, R.; and Firmin, D. In International Conference on Image and Signal Processing, pages 179-190, 2016. Springer International Publishing
Super-Resolved Enhancement of a Single Image and Its Application in Cardiac MRI [pdf]Paper   link   bibtex  
Single-Image Super-Resolution and Its Application in Cardiac MRI: A Feasibility Study. Yang, G.; Ye, X.; Slabaugh, G.; Keegan, J.; Mohiaddin, R.; and Firmin, D. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pages 1, 2016. IEEE
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On the averaging of cardiac diffusion tensor MRI data: The effect of distance function selection. Giannakidis, A.; Melkus, G.; Yang, G.; and Gullberg, G., T. Physics in Medicine and Biology, 61(21): 7765-7786. 2016.
On the averaging of cardiac diffusion tensor MRI data: The effect of distance function selection [pdf]Paper   doi   link   bibtex   abstract  
Morphometric model for discrimination between glioblastoma multiforme and solitary metastasis using three‐dimensional shape analysis. Yang, G.; Jones, T., L.; Howe, F., A.; and Barrick, T., R. Magnetic Resonance in Medicine, 75(6): 2505-2516. 2016.
Morphometric model for discrimination between glioblastoma multiforme and solitary metastasis using three‐dimensional shape analysis [pdf]Paper   link   bibtex  
Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images. Yang, G.; Ye, X.; Slabaugh, G.; Keegan, J.; Mohiaddin, R.; and Firmin, D. Medical Imaging 2016: Image Processing, 9784(March 2016): 97840L. 2016.
Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images [pdf]Paper   doi   link   bibtex   abstract  
  2015 (4)
Discrete Wavelet Transform-Based Whole-Spectral and Subspectral Analysis for Improved Brain Tumor Clustering Using Single Voxel MR Spectroscopy. Yang, G.; Nawaz, T.; Barrick, T., R.; Howe, F., A.; and Slabaugh, G. IEEE Transactions on Biomedical Engineering, 62(12): 2860-2866. 2015.
Discrete Wavelet Transform-Based Whole-Spectral and Subspectral Analysis for Improved Brain Tumor Clustering Using Single Voxel MR Spectroscopy [pdf]Paper   link   bibtex  
An image analysis approach to MRI brain tumour grading. Soltaninejad, M.; Ye, X.; Yang, G.; Allinson, N.; Lambrou, T.; and others Oncology News, 9(6): 204-207. 2015.
An image analysis approach to MRI brain tumour grading [pdf]Paper   link   bibtex  
Analysing MRI Data to Determine Tumour Type. Yang, G.; Barrick, T., R.; Howe, F., A.; Jones, T., L.; and Howe, F., A. WO Patent WO/2015/040,434, 4 2015.
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An image analysis approach to MRI brain tumour grading. Soltaninejad, M.; Ye, X.; Yang, G.; Allinson, N.; and Lambrou, T. Oncology News, 9(6): 204-207. 2015.
An image analysis approach to MRI brain tumour grading [pdf]Paper   link   bibtex  
  2014 (4)
Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique. Jones, T., L.; Byrnes, T., J.; Yang, G.; Howe, F., A.; Bell, B., A.; and Barrick, T., R. Neuro-oncology, 17(3): 466-476. 8 2014.
Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique. [pdf]Paper   Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique. [link]Website   doi   link   bibtex   abstract  
Classification of brain tumour 1 H MR spectra: Extracting features by metabolite quantification or nonlinear manifold learning?. Yang, G.; Raschke, F.; Barrick, T., R.; and Howe, F., A. In Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on, pages 1039-1042, 2014. IEEE
Classification of brain tumour 1 H MR spectra: Extracting features by metabolite quantification or nonlinear manifold learning? [pdf]Paper   link   bibtex  
Brain tumour grading in different MRI protocols using SVM on statistical features. Soltaninejad, M.; Ye, X.; Yang, G.; Allinson, N.; and Lambrou, T. Medical Image Understanding and Analysis,259-264. 2014.
Brain tumour grading in different MRI protocols using SVM on statistical features [pdf]Paper   link   bibtex   abstract  
Discrimination between glioblastoma multiforme and solitary metastasis using morphological features derived from the p:q tensor decomposition of diffusion tensor imaging. Yang, G.; Jones, T., L.; Barrick, T., R.; and Howe, F., A. NMR in Biomedicine, 27(9): 1103-1111. 2014.
Discrimination between glioblastoma multiforme and solitary metastasis using morphological features derived from the p:q tensor decomposition of diffusion tensor imaging [pdf]Paper   link   bibtex  
  2013 (2)
Nonlinear Laplacian Eigenmaps Dimension Reduction of in-vivo Magnetic Resonance Spectroscopic Imaging Analysis. Yang, G.; Raschke, F.; Barrick, T., R.; and Howe, F., A. In International Society for Magnetic Resonance in Medicine (ISMRM) 21st Annual Meeting, pages 1967, 2013. International Society for Magnetic Resonance in Medicine
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Numerical Methods for Coupled Reconstruction and Registration in Digital Breast Tomosynthesis. Yang, G.; Hipwell, J., H.; Hawkes, D., J.; and Arridge, S., R. The Annals of the BMVA, 2013(9): 1-38. 2013.
Numerical Methods for Coupled Reconstruction and Registration in Digital Breast Tomosynthesis [pdf]Paper   Numerical Methods for Coupled Reconstruction and Registration in Digital Breast Tomosynthesis [link]Website   link   bibtex   abstract  
  2012 (3)
Numerical Approaches for Solving the Combined Reconstruction and Registration of Digital Breast Tomosynthesis. Yang, G. Ph.D. Thesis, 2012.
Numerical Approaches for Solving the Combined Reconstruction and Registration of Digital Breast Tomosynthesis [pdf]Paper   link   bibtex  
A Nonlinear Least Squares Method for Solving the Joint Reconstruction and Registration Problem in Digital Breast Tomosynthesis. Yang, G.; Hipwell, J., H.; Hawkes, D., J.; and Arridge, S., R. In Medical Image Understanding and Analysis, pages 87-92, 2012. The British Machine Vision Association
A Nonlinear Least Squares Method for Solving the Joint Reconstruction and Registration Problem in Digital Breast Tomosynthesis [pdf]Paper   link   bibtex  
Joint registration and limited-angle reconstruction of digital breast tomosynthesis. Yang, G.; Hipwell, J., H.; Tanner, C.; Hawkes, D., J.; and Arridge, S., R. In International Workshop on Digital Mammography, pages 713-720, 2012. Springer Berlin Heidelberg
Joint registration and limited-angle reconstruction of digital breast tomosynthesis [pdf]Paper   link   bibtex  
  2011 (2)
Alternating Reconstruction and Registration for Digital Breast Tomosynthesis. Yang, G.; Hipwell, J., H.; Tanner, C.; Hawkes, D., J.; and Arridge, S., R. In Proceedings of the UK Radiological Congress 2011, pages 62-63, 2011. The British Institute of Radiology
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Unconstrained Simultaneous Scheme to Fully Couple Reconstruction and Registration for Digital Breast Tomosynthesis: A Feasible Study. Yang, G.; Hipwell, J., H.; Hawkes, D., J.; and Arridge, S., R. Medical Image Computing and Computer Assisted Intervention MICCAI 2011 Workshop on Breast Image Analysis,25-32. 2011.
Unconstrained Simultaneous Scheme to Fully Couple Reconstruction and Registration for Digital Breast Tomosynthesis: A Feasible Study [pdf]Paper   link   bibtex   abstract  
  2010 (3)
Collaboration of Reconstruction and Registration for Digital Tomosynthesis Application. Yang, G. In 2010 Annual CIMST Meeting, 2010. Eidgenössische Technische Hochschule Zürich (ETH Zürich)
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Combined reconstruction and registration of digital breast tomosynthesis: Sequential method versus iterative method. Yang, G.; Hipwell, J., H.; Clarkson, M., J.; Tanner, C.; Mertzanidou, T.; Gunn, S.; Ourselin, S.; Hawkes, D., J.; and Arridge, S., R. In Medical Image Understanding and Analysis, pages P27-1, 2010. The British Machine Vision Association
Combined reconstruction and registration of digital breast tomosynthesis: Sequential method versus iterative method [pdf]Paper   link   bibtex  
Combined reconstruction and registration of digital breast tomosynthesis. Yang, G.; Hipwell, J., H.; Clarkson, M., J.; Tanner, C.; Mertzanidou, T.; Gunn, S.; Ourselin, S.; Hawkes, D., J.; and Arridge, S., R. In International Workshop on Digital Mammography, pages 760-768, 2010. Springer, Berlin, Heidelberg
Combined reconstruction and registration of digital breast tomosynthesis [pdf]Paper   link   bibtex