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  2025 (2)
Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges. Jha, D.; Sharma, V.; Banik, D.; Bhattacharya, D.; Roy, K.; Hicks, S. A.; Tomar, N. K.; Thambawita, V.; Krenzer, A.; Ji, G.; Poudel, S.; Batchkala, G.; Alam, S.; Ahmed, A. M. A.; Trinh, Q.; Khan, Z.; Nguyen, T.; Shrestha, S.; Nathan, S.; Gwak, J.; Jha, R. K.; Zhang, Z.; Schlaefer, A.; Bhattacharjee, D.; Bhuyan, M. K.; Das, P. K.; Fan, D.; Parasa, S.; Ali, S.; Riegler, M. A.; Halvorsen, P.; de Lange, T.; and Bagci, U. Medical Image Anal., 99: 103307. 2025.
Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges [link]Paper   doi   link   bibtex  
Large-scale multi-center CT and MRI segmentation of pancreas with deep learning. Zhang, Z.; Keles, E.; Durak, G.; Taktak, Y.; Susladkar, O.; Gorade, V.; Jha, D.; Ormeci, A. C.; Medetalibeyoglu, A.; Yao, L.; Wang, B.; Isler, I. S.; Peng, L.; Pan, H.; Vendrami, C. L.; Bourhani, A.; Velichko, Y.; Gong, B.; Spampinato, C.; Pyrros, A.; Tiwari, P.; Klatte, D. C. F.; Engels, M.; Hoogenboom, S.; Bolan, C. W.; Agarunov, E.; Harfouch, N.; Huang, C.; Bruno, M. J.; Schoots, I.; Keswani, R. N.; Miller, F. H.; Gonda, T.; Yazici, C.; Tirkes, T.; Turkbey, B.; Wallace, M. B.; and Bagci, U. Medical Image Anal., 99: 103382. 2025.
Large-scale multi-center CT and MRI segmentation of pancreas with deep learning [link]Paper   doi   link   bibtex  
  2024 (54)
Deformable Capsules for Object Detection. LaLonde, R.; Khosravan, N.; and Bagci, U. Adv. Intell. Syst., 6(9). 2024.
Deformable Capsules for Object Detection [link]Paper   doi   link   bibtex  
Early stage lung cancer detection from speech sounds in natural environments. Ankishan, H.; Ulucanlar, H.; Aktürk, I.; Kavak, K. A.; Bagci, U.; and Yenigün, B. M. Biomed. Signal Process. Control., 96: 106628. 2024.
Early stage lung cancer detection from speech sounds in natural environments [link]Paper   doi   link   bibtex  
AI powered road network prediction with fused low-resolution satellite imagery and GPS trajectory. Gengeç, N. E.; Tari, E.; and Bagci, U. Earth Sci. Informatics, 17(2): 1013–1029. 2024.
AI powered road network prediction with fused low-resolution satellite imagery and GPS trajectory [link]Paper   doi   link   bibtex  
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions. Rauniyar, A.; Hagos, D. H.; Jha, D.; Håkegård, J. E.; Bagci, U.; Rawat, D. B.; and Vlassov, V. IEEE Internet Things J., 11(5): 7374–7398. 2024.
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions [link]Paper   doi   link   bibtex  
COVID-19 Detection From Respiratory Sounds With Hierarchical Spectrogram Transformers. Aytekin, I.; Dalmaz, O.; Gonc, K.; Ankishan, H.; Saritas, E. U.; Bagci, U.; Celik, H.; and Çukur, T. IEEE J. Biomed. Health Informatics, 28(3): 1273–1284. 2024.
COVID-19 Detection From Respiratory Sounds With Hierarchical Spectrogram Transformers [link]Paper   doi   link   bibtex  
PGDS: Pose-Guidance Deep Supervision for Mitigating Clothes-Changing in Person Re-Identification. Trinh, Q.; Bui, N.; Hoang, D.; Thi, P. V.; Nguyen, H.; Jha, D.; Bagci, U.; Le, N.; and Tran, M. In IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2024, Niagara Falls, ON, Canada, July 15-16, 2024, pages 1–8, 2024.
PGDS: Pose-Guidance Deep Supervision for Mitigating Clothes-Changing in Person Re-Identification [link]Paper   doi   link   bibtex  
Prediction of MRI-Induced Power Absorption in Patients with DBS Leads. Tur, Y.; Vu, J.; Waktola, S.; Medetalibeyoglu, A.; Golestanirad, L.; and Bagci, U. In 37th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2024, Guadalajara, Mexico, June 26-28, 2024, pages 490–495, 2024.
Prediction of MRI-Induced Power Absorption in Patients with DBS Leads [link]Paper   doi   link   bibtex  
ControlPolypNet: Towards Controlled Colon Polyp Synthesis for Improved Polyp Segmentation. Sharma, V.; Kumar, A.; Jha, D.; Bhuyan, M. K.; Das, P. K.; and Bagci, U. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 - Workshops, Seattle, WA, USA, June 17-18, 2024, pages 2325–2334, 2024.
ControlPolypNet: Towards Controlled Colon Polyp Synthesis for Improved Polyp Segmentation [link]Paper   doi   link   bibtex  
PP-SAM: Perturbed Prompts for Robust Adaption of Segment Anything Model for Polyp Segmentation. Rahman, M. M.; Munir, M.; Jha, D.; Bagci, U.; and Marculescu, R. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 - Workshops, Seattle, WA, USA, June 17-18, 2024, pages 4989–4995, 2024.
PP-SAM: Perturbed Prompts for Robust Adaption of Segment Anything Model for Polyp Segmentation [link]Paper   doi   link   bibtex  
Domain Generalization with fourier Transform and soft thresholding. Pan, H.; Wang, B.; Zhang, Z.; Zhu, X.; Jha, D.; Çetin, A. E.; Spampinato, C.; and Bagci, U. In IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2024, Seoul, Republic of Korea, April 14-19, 2024, pages 2106–2110, 2024.
Domain Generalization with fourier Transform and soft thresholding [link]Paper   doi   link   bibtex  
Harmonized Spatial and Spectral Learning for Generalized Medical Image Segmentation. Gorade, V.; Mittal, S.; Jha, D.; Singhal, R.; and Bagci, U. In Pattern Recognition - 27th International Conference, ICPR 2024, Kolkata, India, December 1-5, 2024, Proceedings, Part XIII, pages 178–193, 2024.
Harmonized Spatial and Spectral Learning for Generalized Medical Image Segmentation [link]Paper   doi   link   bibtex  
Evidential Federated Learning for Skin Lesion Image Classification. Hendrix, R.; Salanitri, F. P.; Spampinato, C.; Palazzo, S.; and Bagci, U. In Pattern Recognition - 27th International Conference, ICPR 2024, Kolkata, India, December 1-5, 2024, Proceedings, Part XXIX, pages 354–365, 2024.
Evidential Federated Learning for Skin Lesion Image Classification [link]Paper   doi   link   bibtex  
ProFONet: Prototypical Feature Space Optimized Network for Few Shot Classification. Das, A.; Gorade, V.; Jha, D.; Biswas, K.; Raj, P.; and Bagci, U. In Pattern Recognition - 27th International Conference, ICPR 2024, Kolkata, India, December 1-5, 2024, Proceedings, Part VII, pages 392–406, 2024.
ProFONet: Prototypical Feature Space Optimized Network for Few Shot Classification [link]Paper   doi   link   bibtex  
HCA-NET: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling. Bozorgpour, A.; Azad, B.; Azad, R.; Velichko, Y.; Bagci, U.; and Merhof, D. In IEEE International Symposium on Biomedical Imaging, ISBI 2024, Athens, Greece, May 27-30, 2024, pages 1–5, 2024.
HCA-NET: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling [link]Paper   doi   link   bibtex  
Explainable Transformer Prototypes for Medical Diagnoses. Demir, U.; Jha, D.; Zhang, Z.; Keles, E.; Allen, B. D.; Katsaggelos, A. K.; and Bagci, U. In IEEE International Symposium on Biomedical Imaging, ISBI 2024, Athens, Greece, May 27-30, 2024, pages 1–5, 2024.
Explainable Transformer Prototypes for Medical Diagnoses [link]Paper   doi   link   bibtex   1 download  
Rethinking Intermediate Layers Design in Knowledge Distillation for Kidney and Liver Tumor Segmentation. Gorade, V.; Mittal, S.; Jha, D.; and Bagci, U. In IEEE International Symposium on Biomedical Imaging, ISBI 2024, Athens, Greece, May 27-30, 2024, pages 1–6, 2024.
Rethinking Intermediate Layers Design in Knowledge Distillation for Kidney and Liver Tumor Segmentation [link]Paper   doi   link   bibtex  
CT Liver Segmentation Via PVT-Based Encoding and Refined Decoding. Jha, D.; Tomar, N. K.; Biswas, K.; Durak, G.; Medetalibeyoglu, A.; Antalek, M.; Velichko, Y.; Ladner, D. P.; Borhani, A.; and Bagci, U. In IEEE International Symposium on Biomedical Imaging, ISBI 2024, Athens, Greece, May 27-30, 2024, pages 1–5, 2024.
CT Liver Segmentation Via PVT-Based Encoding and Refined Decoding [link]Paper   doi   link   bibtex  
Leveraging Unlabeled Data for 3D Medical Image Segmentation Through Self-Supervised Contrastive Learning. Karimijafarbigloo, S.; Azad, R.; Velichko, Y.; Bagci, U.; and Merhof, D. In IEEE International Symposium on Biomedical Imaging, ISBI 2024, Athens, Greece, May 27-30, 2024, pages 1–5, 2024.
Leveraging Unlabeled Data for 3D Medical Image Segmentation Through Self-Supervised Contrastive Learning [link]Paper   doi   link   bibtex  
Fusenet: Self-Supervised Dual-Path Network For Medical Image Segmentation. Kazerouni, A.; Karimijafarbigloo, S.; Azad, R.; Velichko, Y.; Bagci, U.; and Merhof, D. In IEEE International Symposium on Biomedical Imaging, ISBI 2024, Athens, Greece, May 27-30, 2024, pages 1–5, 2024.
Fusenet: Self-Supervised Dual-Path Network For Medical Image Segmentation [link]Paper   doi   link   bibtex  
Classification of Endoscopy and Video Capsule Images Using CNN-Transformer Model. Subedi, A.; Regmi, S.; Regmi, N.; Bhusal, B.; Bagci, U.; and Jha, D. In Cancer Prevention, Detection, and Intervention - Third MICCAI Workshop, CaPTion 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings, pages 26–36, 2024.
Classification of Endoscopy and Video Capsule Images Using CNN-Transformer Model [link]Paper   doi   link   bibtex  
Optimizing Synthetic Data for Enhanced Pancreatic Tumor Segmentation. Peng, L.; Zhang, Z.; Durak, G.; Miller, F. H.; Medetalibeyoglu, A.; Wallace, M. B.; and Bagci, U. In Artificial Intelligence in Pancreatic Disease Detection and Diagnosis, and Personalized Incremental Learning in Medicine - First International Workshop, AIPAD 2024 and First International Workshop, PILM 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings, pages 35–44, 2024.
Optimizing Synthetic Data for Enhanced Pancreatic Tumor Segmentation [link]Paper   doi   link   bibtex  
Adaptive Smooth Activation Function for Improved Organ Segmentation and Disease Diagnosis. Biswas, K.; Jha, D.; Tomar, N. K.; Karri, M.; Reza, A.; Durak, G.; Medetalibeyoglu, A.; Antalek, M.; Velichko, Y.; Ladner, D. P.; Borhani, A.; and Bagci, U. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2024 - 27th International Conference, Marrakesh, Morocco, October 6-10, 2024, Proceedings, Part IX, pages 65–74, 2024.
Adaptive Smooth Activation Function for Improved Organ Segmentation and Disease Diagnosis [link]Paper   doi   link   bibtex  
Practical and Ethical Considerations for Generative AI in Medical Imaging. Jha, D.; Rauniyar, A.; Hagos, D. H.; Sharma, V.; Tomar, N. K.; Zhang, Z.; Isler, I.; Durak, G.; Wallace, M. B.; Yazici, C.; Berzin, T. M.; Biswas, K.; and Bagci, U. In Ethics and Fairness in Medical Imaging - Second International Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and Third International Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6-10, 2024, Proceedings, pages 176–187, 2024.
Practical and Ethical Considerations for Generative AI in Medical Imaging [link]Paper   doi   link   bibtex  
A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation. Biswas, K.; Pal, R.; Patel, S.; Jha, D.; Karri, M.; Reza, A.; Durak, G.; Medetalibeyoglu, A.; Antalek, M.; Velichko, Y.; Ladner, D. P.; Borhani, A.; and Bagci, U. In Machine Learning in Medical Imaging - 15th International Workshop, MLMI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings, Part I, pages 1–11, 2024.
A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation [link]Paper   doi   link   bibtex  
A Probabilistic Hadamard U-Net for MRI Bias Field Correction. Zhu, X.; Pan, H.; Gundogdu, B.; Jha, D.; Velichko, Y.; Murphy, A. B.; Ross, A.; Turkbey, B.; Çetin, A. E.; and Bagci, U. In Machine Learning in Medical Imaging - 15th International Workshop, MLMI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings, Part I, pages 208–217, 2024.
A Probabilistic Hadamard U-Net for MRI Bias Field Correction [link]Paper   doi   link   bibtex  
Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation. Azad, R.; Niggemeier, L.; Hüttemann, M.; Kazerouni, A.; Aghdam, E. K.; Velichko, Y.; Bagci, U.; and Merhof, D. In IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2024, Waikoloa, HI, USA, January 3-8, 2024, pages 1276–1286, 2024.
Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation [link]Paper   doi   link   bibtex  
INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings. Kazerouni, A.; Azad, R.; Hosseini, A.; Merhof, D.; and Bagci, U. In IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2024, Waikoloa, HI, USA, January 3-8, 2024, pages 1287–1296, 2024.
INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings [link]Paper   doi   link   bibtex  
Domain Generalization with Correlated Style Uncertainty. Zhang, Z.; Wang, B.; Jha, D.; Demir, U.; and Bagci, U. In IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2024, Waikoloa, HI, USA, January 3-8, 2024, pages 1989–1998, 2024.
Domain Generalization with Correlated Style Uncertainty [link]Paper   doi   link   bibtex  
GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification. Wang, B.; Pan, H.; Aboah, A.; Zhang, Z.; Keles, E.; Torigian, D. A.; Turkbey, B.; Krupinski, E. A.; Udupa, J. K.; and Bagci, U. In IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2024, Waikoloa, HI, USA, January 3-8, 2024, pages 2183–2192, 2024.
GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification [link]Paper   doi   link   bibtex  
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation. Gorade, V.; Mittal, S.; Jha, D.; and Bagci, U. In IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2024, Waikoloa, HI, USA, January 3-8, 2024, pages 7753–7762, 2024.
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation [link]Paper   doi   link   bibtex  
CT Liver Segmentation via PVT-based Encoding and Refined Decoding. Jha, D.; Tomar, N. K.; Biswas, K.; Durak, G.; Medetalibeyoglu, A.; Antalek, M.; Velichko, Y.; Ladner, D. P.; Borhani, A.; and Bagci, U. CoRR, abs/2401.09630. 2024.
CT Liver Segmentation via PVT-based Encoding and Refined Decoding [link]Paper   doi   link   bibtex  
Harmonized Spatial and Spectral Learning for Robust and Generalized Medical Image Segmentation. Gorade, V.; Mittal, S.; Jha, D.; Singhal, R.; and Bagci, U. CoRR, abs/2401.10373. 2024.
Harmonized Spatial and Spectral Learning for Robust and Generalized Medical Image Segmentation [link]Paper   doi   link   bibtex  
A Probabilistic Hadamard U-Net for MRI Bias Field Correction. Zhu, X.; Pan, H.; Velichko, Y.; Murphy, A. B.; Ross, A.; Turkbey, B.; Çetin, A. E.; and Bagci, U. CoRR, abs/2403.05024. 2024.
A Probabilistic Hadamard U-Net for MRI Bias Field Correction [link]Paper   doi   link   bibtex  
Explainable Transformer Prototypes for Medical Diagnoses. Demir, U.; Jha, D.; Zhang, Z.; Keles, E.; Allen, B. D.; Katsaggelos, A. K.; and Bagci, U. CoRR, abs/2403.06961. 2024.
Explainable Transformer Prototypes for Medical Diagnoses [link]Paper   doi   link   bibtex   1 download  
Detection of Peri-Pancreatic Edema using Deep Learning and Radiomics Techniques. Hong, Z.; Jha, D.; Biswas, K.; Zhang, Z.; Velichko, Y.; Yazici, C.; Tirkes, T.; Borhani, A.; Turkbey, B.; Medetalibeyoglu, A.; Durak, G.; and Bagci, U. CoRR, abs/2404.17064. 2024.
Detection of Peri-Pancreatic Edema using Deep Learning and Radiomics Techniques [link]Paper   doi   link   bibtex  
PAM-UNet: Shifting Attention on Region of Interest in Medical Images. Das, A.; Jha, D.; Gorade, V.; Biswas, K.; Pan, H.; Zhang, Z.; Ladner, D. P.; Velichko, Y.; Borhani, A.; and Bagci, U. CoRR, abs/2405.01503. 2024.
PAM-UNet: Shifting Attention on Region of Interest in Medical Images [link]Paper   doi   link   bibtex  
MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation. Jha, D.; Tomar, N. K.; Biswas, K.; Durak, G.; Antalek, M.; Zhang, Z.; Wang, B.; Rahman, M. M.; Pan, H.; Medetalibeyoglu, A.; Velichko, Y.; Ladner, D. P.; Borhani, A.; and Bagci, U. CoRR, abs/2405.06166. 2024.
MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation [link]Paper   doi   link   bibtex  
Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning. Zhang, Z.; Keles, E.; Durak, G.; Taktak, Y.; Susladkar, O.; Gorade, V.; Jha, D.; Ormeci, A. C.; Medetalibeyoglu, A.; Yao, L.; Wang, B.; Isler, I. S.; Peng, L.; Pan, H.; Vendrami, C. L.; Bourhani, A.; Velichko, Y.; Gong, B.; Spampinato, C.; Pyrros, A.; Tiwari, P.; Klatte, D. C. F.; Engels, M.; Hoogenboom, S.; Bolan, C. W.; Agarunov, E.; Harfouch, N.; Huang, C.; Bruno, M. J.; Schoots, I.; Keswani, R. N.; Miller, F. H.; Gonda, T.; Yazici, C.; Tirkes, T.; Turkbey, B.; Wallace, M. B.; and Bagci, U. CoRR, abs/2405.12367. 2024.
Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning [link]Paper   doi   link   bibtex  
DCT-Based Decorrelated Attention for Vision Transformers. Pan, H.; Hamdan, E.; Zhu, X.; Biswas, K.; Çetin, A. E.; and Bagci, U. CoRR, abs/2405.13901. 2024.
DCT-Based Decorrelated Attention for Vision Transformers [link]Paper   doi   link   bibtex  
PP-SAM: Perturbed Prompts for Robust Adaptation of Segment Anything Model for Polyp Segmentation. Rahman, M. M.; Munir, M.; Jha, D.; Bagci, U.; and Marculescu, R. CoRR, abs/2405.16740. 2024.
PP-SAM: Perturbed Prompts for Robust Adaptation of Segment Anything Model for Polyp Segmentation [link]Paper   doi   link   bibtex  
Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation. LaBella, D.; Schumacher, K.; Mix, M.; Leu, K.; McBurney-Lin, S.; Nedelec, P.; Villanueva-Meyer, J. E.; Shapey, J.; Vercauteren, T.; Chia, K.; Al-Salihi, O.; Leu, J.; Halasz, L.; Velichko, Y.; Wang, C.; Kirkpatrick, J.; Floyd, S.; Reitman, Z. J.; Mullikin, T.; Bagci, U.; Sachdev, S.; Hattangadi-Gluth, J. A.; Seibert, T. M.; Farid, N.; Puett, C.; Pease, M. W.; Shiue, K.; Anwar, S. M.; Faghani, S.; Haider, M. A.; Warman, P. I.; Albrecht, J.; Jakab, A.; Moassefi, M.; Chung, V.; Aristizabal, A.; Karargyris, A.; Kassem, H.; Pati, S.; Sheller, M. J.; Huang, C.; Coley, A.; Ghanta, S.; Schneider, A.; Sharp, C.; Saluja, R.; Kofler, F.; Lohmann, P.; Vollmuth, P.; Gagnon, L.; Adewole, M.; Li, H. B.; Kazerooni, A. F.; Tahon, N. H.; Anazodo, U.; Moawad, A. W.; Menze, B. H.; Linguraru, M. G.; Aboian, M.; Wiestler, B.; Baid, U.; Conte, G. M.; Rauschecker, A. M.; Nada, A.; Abayazeed, A. H.; Huang, R. Y.; de Verdier, M. C.; Rudie, J. D.; Bakas, S.; and Calabrese, E. CoRR, abs/2405.18383. 2024.
Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation [link]Paper   doi   link   bibtex  
Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis. Heidari, M.; Kolahi, S. G.; Karimijafarbigloo, S.; Azad, B.; Bozorgpour, A.; Hatami, S.; Azad, R.; Diba, A.; Bagci, U.; Merhof, D.; and Hacihaliloglu, I. CoRR, abs/2406.03430. 2024.
Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis [link]Paper   doi   link   bibtex  
Ethical Framework for Responsible Foundational Models in Medical Imaging. Das, A.; Jha, D.; Sanjotra, J.; Susladkar, O.; Sarkar, S.; Rauniyar, A.; Tomar, N. K.; Sharma, V.; and Bagci, U. CoRR, abs/2406.11868. 2024.
Ethical Framework for Responsible Foundational Models in Medical Imaging [link]Paper   doi   link   bibtex  
SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation. Trinh, Q.; Nguyen, H.; Ngoc, B. N.; Jha, D.; Bagci, U.; and Tran, M. CoRR, abs/2406.14819. 2024.
SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation [link]Paper   doi   link   bibtex  
Optimizing Synthetic Data for Enhanced Pancreatic Tumor Segmentation. Peng, L.; Zhang, Z.; Durak, G.; Miller, F. H.; Medetalibeyoglu, A.; Wallace, M. B.; and Bagci, U. CoRR, abs/2407.19284. 2024.
Optimizing Synthetic Data for Enhanced Pancreatic Tumor Segmentation [link]Paper   doi   link   bibtex  
Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs. Gorade, V.; Susladkar, O.; Durak, G.; Keles, E.; Aktas, E.; Cebeci, T.; Medetalibeyoglu, A.; Ladner, D. P.; Jha, D.; and Bagci, U. CoRR, abs/2408.04491. 2024.
Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs [link]Paper   doi   link   bibtex  
A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation. Biswas, K.; Pal, R.; Patel, S.; Jha, D.; Karri, M.; Reza, A.; Durak, G.; Medetalibeyoglu, A.; Antalek, M.; Velichko, Y.; Ladner, D. P.; Borhani, A.; and Bagci, U. CoRR, abs/2408.05692. 2024.
A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation [link]Paper   doi   link   bibtex  
Classification of Endoscopy and Video Capsule Images using CNN-Transformer Model. Subedi, A.; Regmi, S.; Regmi, N.; Bhusal, B.; Bagci, U.; and Jha, D. CoRR, abs/2408.10733. 2024.
Classification of Endoscopy and Video Capsule Images using CNN-Transformer Model [link]Paper   doi   link   bibtex  
PolypDB: A Curated Multi-Center Dataset for Development of AI Algorithms in Colonoscopy. Jha, D.; Tomar, N. K.; Sharma, V.; Trinh, Q.; Biswas, K.; Pan, H.; Jha, R. K.; Durak, G.; Hann, A.; Varkey, J.; Dao, H. V.; Dao, L. V.; Nguyen, B. P.; Pham, K. C.; Tran, Q. T.; Papachrysos, N.; Rieders, B.; Schmidt, P. T.; Geissler, E.; Berzin, T. M.; Halvorsen, P.; Riegler, M. A.; de Lange, T.; and Bagci, U. CoRR, abs/2409.00045. 2024.
PolypDB: A Curated Multi-Center Dataset for Development of AI Algorithms in Colonoscopy [link]Paper   doi   link   bibtex  
Transformer-Enhanced Iterative Feedback Mechanism for Polyp Segmentation. Tomar, N. K.; Jha, D.; Biswas, K.; Berzin, T. M.; Keswani, R.; Wallace, M. B.; and Bagci, U. CoRR, abs/2409.05875. 2024.
Transformer-Enhanced Iterative Feedback Mechanism for Polyp Segmentation [link]Paper   doi   link   bibtex  
Order-aware Interactive Segmentation. Wang, B.; Choudhuri, A.; Zheng, M.; Gao, Z.; Planche, B.; Deng, A.; Liu, Q.; Chen, T.; Bagci, U.; and Wu, Z. CoRR, abs/2410.12214. 2024.
Order-aware Interactive Segmentation [link]Paper   doi   link   bibtex  
CirrMRI600+: Large Scale MRI Collection and Segmentation of Cirrhotic Liver. Jha, D.; Susladkar, O.; Gorade, V.; Keles, E.; Antalek, M.; Seyithanoglu, D.; Cebeci, T.; Aktas, E.; Kartal, G. D.; Kaymakoglu, S.; Erturk, S. M.; Velichko, Y. S.; Ladner, D. P.; Borhani, A.; Medetalibeyoglu, A.; Durak, G.; and Bagci, U. CoRR, abs/2410.16296. 2024.
CirrMRI600+: Large Scale MRI Collection and Segmentation of Cirrhotic Liver [link]Paper   doi   link   bibtex  
Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI. Pan, H.; Durak, G.; Zhang, Z.; Taktak, Y.; Keles, E.; Aktas, E.; Medetalibeyoglu, A.; Velichko, Y.; Spampinato, C.; Schoots, I.; Bruno, M. J.; Keswani, R. N.; Tiwari, P.; Bolan, C. W.; Gonda, T.; Goggins, M. G.; Wallace, M. B.; Xu, Z.; and Bagci, U. CoRR, abs/2410.22530. 2024.
Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI [link]Paper   doi   link   bibtex  
A New Logic For Pediatric Brain Tumor Segmentation. Bengtsson, M.; Keles, E.; Durak, G.; Anwar, S.; Velichko, Y. S.; Linguraru, M. G.; Waanders, A. J.; and Bagci, U. CoRR, abs/2411.01390. 2024.
A New Logic For Pediatric Brain Tumor Segmentation [link]Paper   doi   link   bibtex  
  2023 (55)
Monkeypox Diagnosis With Interpretable Deep Learning. Ahsan, M. M.; Ali, M. S.; Hassan, M. M.; Abdullah, T. A.; Gupta, K. D.; Bagci, U.; Kaushal, C.; and Soliman, N. F. IEEE Access, 11: 81965–81980. 2023.
Monkeypox Diagnosis With Interpretable Deep Learning [link]Paper   doi   link   bibtex  
An automatic segmentation framework for computer-assisted renal scintigraphy procedure. Rahimi, A.; Hosntalab, M.; Mofrad, F. B.; Amoui, M.; and Bagci, U. Medical Biol. Eng. Comput., 61(1): 285–295. 2023.
An automatic segmentation framework for computer-assisted renal scintigraphy procedure [link]Paper   doi   link   bibtex  
The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review. Keles, E.; and Bagci, U. npj Digit. Medicine, 6. 2023.
The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review [link]Paper   doi   link   bibtex  
Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8. Aboah, A.; Wang, B.; Bagci, U.; and Adu-Gyamfi, Y. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 - Workshops, Vancouver, BC, Canada, June 17-24, 2023, pages 5350–5358, 2023.
Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8 [link]Paper   doi   link   bibtex  
DeepSegmenter: Temporal Action Localization for Detecting Anomalies in Untrimmed Naturalistic Driving Videos. Aboah, A.; Bagci, U.; Mussah, A. R.; Owor, N. J.; and Adu-Gyamfi, Y. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 - Workshops, Vancouver, BC, Canada, June 17-24, 2023, pages 5359–5365, 2023.
DeepSegmenter: Temporal Action Localization for Detecting Anomalies in Untrimmed Naturalistic Driving Videos [link]Paper   doi   link   bibtex  
Gastrointestinal Disease Diagnosis with Hybrid Model of Capsules and CNNs. Sarsengeldin, M.; Imatayeva, S.; Abeuov, N.; Naukhanov, M.; Erdogan, A. S.; Jha, D.; and Bagci, U. In IEEE International Conference on Electro Information Technology, eIT 2023, Romeoville, IL, USA, May 18-20, 2023, pages 143–146, 2023.
Gastrointestinal Disease Diagnosis with Hybrid Model of Capsules and CNNs [link]Paper   doi   link   bibtex  
An Efficient Multi-Scale Fusion Network for 3D Organs at Risk (OARs) Segmentation. Srivastava, A.; Jha, D.; Keles, E.; Aydogan, B.; Abazeed, M.; and Bagci, U. In 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2023, Sydney, Australia, July 24-27, 2023, pages 1–4, 2023.
An Efficient Multi-Scale Fusion Network for 3D Organs at Risk (OARs) Segmentation [link]Paper   doi   link   bibtex  
TransResU-Net: A Transformer based ResU-Net for Real-Time Colon Polyp Segmentation. Tomar, N. K.; Shergill, A.; Rieders, B.; Bagci, U.; and Jha, D. In 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2023, Sydney, Australia, July 24-27, 2023, pages 1–4, 2023.
TransResU-Net: A Transformer based ResU-Net for Real-Time Colon Polyp Segmentation [link]Paper   doi   link   bibtex  
GazeSAM: Interactive Image Segmentation with Eye Gaze and Segment Anything Model. Wang, B.; Aboah, A.; Zhang, Z.; Pan, H.; and Bagci, U. In Gaze Meets Machine Learning Workshop, 16 December 2023, New Orleans, Louisiana, USA, pages 254–265, 2023.
GazeSAM: Interactive Image Segmentation with Eye Gaze and Segment Anything Model [link]Paper   link   bibtex  
Self-supervised Semantic Segmentation: Consistency over Transformation. Karimijafarbigloo, S.; Azad, R.; Kazerouni, A.; Velichko, Y.; Bagci, U.; and Merhof, D. In IEEE/CVF International Conference on Computer Vision, ICCV 2023 - Workshops, Paris, France, October 2-6, 2023, pages 2646–2655, 2023.
Self-supervised Semantic Segmentation: Consistency over Transformation [link]Paper   doi   link   bibtex  
Object Localization Using Non-Euclidean Metrics. Bagci, U.; Udupa, J. K.; Chen, X.; Torigian, D. A.; Medetalibeyoglu, A.; Zhou, D.; and Bai, L. In IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023, Bali, Indonesia, November 28-30, 2023, pages 1–7, 2023.
Object Localization Using Non-Euclidean Metrics [link]Paper   doi   link   bibtex  
Ensemble Learning with Residual Transformer for Brain Tumor Segmentation. Yao, L.; Zhang, Z.; and Bagci, U. In 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023, Cartagena, Colombia, April 18-21, 2023, pages 1–5, 2023.
Ensemble Learning with Residual Transformer for Brain Tumor Segmentation [link]Paper   doi   link   bibtex  
RUPNet: residual upsampling network for real-time polyp segmentation. Tomar, N. K.; Bagci, U.; and Jha, D. In Medical Imaging 2023: Computer-Aided Diagnosis, San Diego, CA, USA, February 19-23, 2023, 2023.
RUPNet: residual upsampling network for real-time polyp segmentation [link]Paper   doi   link   bibtex  
A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications. Pennisi, M.; Salanitri, F. P.; Bellitto, G.; Palazzo, S.; Bagci, U.; and Spampinato, C. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 - 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part III, pages 422–431, 2023.
A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications [link]Paper   doi   link   bibtex  
Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection. Azad, R.; Kazerouni, A.; Azad, B.; Aghdam, E. K.; Velichko, Y.; Bagci, U.; and Merhof, D. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 - 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part III, pages 736–746, 2023.
Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection [link]Paper   doi   link   bibtex  
TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing. Jha, D.; Tomar, N. K.; Sharma, V.; and Bagci, U. In Medical Imaging with Deep Learning, MIDL 2023, 10-12 July 2023, Nashville, TN, USA, pages 1372–1384, 2023.
TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing [link]Paper   link   bibtex  
GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection. Jha, D.; Sharma, V.; Dasu, N.; Tomar, N. K.; Hicks, S.; Bhuyan, M. K.; Das, P. K.; Riegler, M. A.; Halvorsen, P.; Bagci, U.; and de Lange, T. In Machine Learning for Multimodal Healthcare Data - First International Workshop, ML4MHD 2023, Honolulu, Hawaii, USA, July 29, 2023, Proceedings, pages 125–140, 2023.
GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection [link]Paper   doi   link   bibtex  
Radiomics Boosts Deep Learning Model for IPMN Classification. Yao, L.; Zhang, Z.; Demir, U.; Keles, E.; Vendrami, C.; Agarunov, E.; Bolan, C. W.; Schoots, I.; Bruno, M. J.; Keswani, R.; Miller, F. H.; Gonda, T.; Yazici, C.; Tirkes, T.; Wallace, M. B.; Spampinato, C.; and Bagci, U. In Machine Learning in Medical Imaging - 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings, Part II, pages 134–143, 2023.
Radiomics Boosts Deep Learning Model for IPMN Classification [link]Paper   doi   link   bibtex  
DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation. Tomar, N. K.; Jha, D.; and Bagci, U. In MultiMedia Modeling - 29th International Conference, MMM 2023, Bergen, Norway, January 9-12, 2023, Proceedings, Part I, pages 334–344, 2023.
DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation [link]Paper   doi   link   bibtex  
A Critical Appraisal of Data Augmentation Methods for Imaging-Based Medical Diagnosis Applications. Pattilachan, T. M.; Demir, U.; Keles, E.; Jha, D.; Klatte, D. C. F.; Engels, M.; Hoogenboom, S.; Bolan, C. W.; Wallace, M. B.; and Bagci, U. CoRR, abs/2301.02181. 2023.
A Critical Appraisal of Data Augmentation Methods for Imaging-Based Medical Diagnosis Applications [link]Paper   doi   link   bibtex  
RUPNet: Residual upsampling network for real-time polyp segmentation. Tomar, N. K.; Bagci, U.; and Jha, D. CoRR, abs/2301.02703. 2023.
RUPNet: Residual upsampling network for real-time polyp segmentation [link]Paper   doi   link   bibtex  
The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence. Keles, E.; and Bagci, U. CoRR, abs/2302.00225. 2023.
The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence [link]Paper   doi   link   bibtex  
Selecting the Best Optimizers for Deep Learning based Medical Image Segmentation. Mortazi, A.; Cicek, V.; Keles, E.; and Bagci, U. CoRR, abs/2302.02289. 2023.
Selecting the Best Optimizers for Deep Learning based Medical Image Segmentation [link]Paper   doi   link   bibtex  
TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing. Jha, D.; Tomar, N. K.; Sharma, V.; and Bagci, U. CoRR, abs/2303.07428. 2023.
TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing [link]Paper   doi   link   bibtex  
Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentation. Zhang, Z.; Wang, B.; Yao, L.; Demir, U.; Jha, D.; Turkbey, I. B.; Gong, B.; and Bagci, U. CoRR, abs/2304.02720. 2023.
Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentation [link]Paper   doi   link   bibtex  
Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8. Aboah, A.; Wang, B.; Bagci, U.; and Adu-Gyamfi, Y. CoRR, abs/2304.08256. 2023.
Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8 [link]Paper   doi   link   bibtex  
DeepSegmenter: Temporal Action Localization for Detecting Anomalies in Untrimmed Naturalistic Driving Videos. Aboah, A.; Bagci, U.; Mussah, A. R.; Owor, N. J.; and Adu-Gyamfi, Y. CoRR, abs/2304.08261. 2023.
DeepSegmenter: Temporal Action Localization for Detecting Anomalies in Untrimmed Naturalistic Driving Videos [link]Paper   doi   link   bibtex  
Vision Transformer for Efficient Chest X-ray and Gastrointestinal Image Classification. Regmi, S.; Subedi, A.; Bagci, U.; and Jha, D. CoRR, abs/2304.11529. 2023.
Vision Transformer for Efficient Chest X-ray and Gastrointestinal Image Classification [link]Paper   doi   link   bibtex  
Ensuring Trustworthy Medical Artificial Intelligence through Ethical and Philosophical Principles. Jha, D.; Rauniyar, A.; Srivastava, A.; Hagos, D. H.; Tomar, N. K.; Sharma, V.; Keles, E.; Zhang, Z.; Demir, U.; Topcu, A.; Yazidi, A.; Håkegård, J. E.; and Bagci, U. CoRR, abs/2304.11530. 2023.
Ensuring Trustworthy Medical Artificial Intelligence through Ethical and Philosophical Principles [link]Paper   doi   link   bibtex  
GazeSAM: What You See is What You Segment. Wang, B.; Aboah, A.; Zhang, Z.; and Bagci, U. CoRR, abs/2304.13844. 2023.
GazeSAM: What You See is What You Segment [link]Paper   doi   link   bibtex  
Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification. Isler, I.; Jha, D.; Lisle, C.; Rineer, J.; Kelly, P.; Aydogan, B.; Abazeed, M.; Turgut, D.; and Bagci, U. CoRR, abs/2305.02491. 2023.
Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification [link]Paper   doi   link   bibtex  
GazeGNN: A Gaze-Guided Graph Neural Network for Disease Classification. Wang, B.; Pan, H.; Aboah, A.; Zhang, Z.; Çetin, A. E.; Torigian, D. A.; Turkbey, B.; Krupinski, E. A.; Udupa, J. K.; and Bagci, U. CoRR, abs/2305.18221. 2023.
GazeGNN: A Gaze-Guided Graph Neural Network for Disease Classification [link]Paper   doi   link   bibtex  
TransRUPNet for Improved Out-of-Distribution Generalization in Polyp Segmentation. Jha, D.; Tomar, N. K.; and Bagci, U. CoRR, abs/2306.02176. 2023.
TransRUPNet for Improved Out-of-Distribution Generalization in Polyp Segmentation [link]Paper   doi   link   bibtex  
A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications. Pennisi, M.; Salanitri, F. P.; Bellitto, G.; Palazzo, S.; Bagci, U.; and Spampinato, C. CoRR, abs/2307.02984. 2023.
A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications [link]Paper   doi   link   bibtex  
GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection. Jha, D.; Sharma, V.; Dasu, N.; Tomar, N. K.; Hicks, S.; Bhuyan, M. K.; Das, P. K.; Riegler, M. A.; Halvorsen, P.; de Lange, T.; and Bagci, U. CoRR, abs/2307.08140. 2023.
GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection [link]Paper   doi   link   bibtex  
An objective validation of polyp and instrument segmentation methods in colonoscopy through Medico 2020 polyp segmentation and MedAI 2021 transparency challenges. Jha, D.; Sharma, V.; Banik, D.; Bhattacharya, D.; Roy, K.; Hicks, S. A.; Tomar, N. K.; Thambawita, V.; Krenzer, A.; Ji, G.; Poudel, S.; Batchkala, G.; Alam, S.; Ahmed, A. M. A.; Trinh, Q.; Khan, Z.; Nguyen, T.; Shrestha, S.; Nathan, S.; Gwak, J.; Jha, R. K.; Zhang, Z.; Schlaefer, A.; Bhattacharjee, D.; Bhuyan, M. K.; Das, P. K.; Parasa, S.; Ali, S.; Riegler, M. A.; Halvorsen, P.; Bagci, U.; and de Lange, T. CoRR, abs/2307.16262. 2023.
An objective validation of polyp and instrument segmentation methods in colonoscopy through Medico 2020 polyp segmentation and MedAI 2021 transparency challenges [link]Paper   doi   link   bibtex  
Ensemble Learning with Residual Transformer for Brain Tumor Segmentation. Yao, L.; Zhang, Z.; and Bagci, U. CoRR, abs/2308.00128. 2023.
Ensemble Learning with Residual Transformer for Brain Tumor Segmentation [link]Paper   doi   link   bibtex  
Prototype Learning for Out-of-Distribution Polyp Segmentation. Tomar, N. K.; Jha, D.; and Bagci, U. CoRR, abs/2308.03709. 2023.
Prototype Learning for Out-of-Distribution Polyp Segmentation [link]Paper   doi   link   bibtex  
Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection. Azad, R.; Kazerouni, A.; Azad, B.; Aghdam, E. K.; Velichko, Y.; Bagci, U.; and Merhof, D. CoRR, abs/2309.00108. 2023.
Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection [link]Paper   doi   link   bibtex  
Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation. Azad, R.; Niggemeier, L.; Huttemann, M.; Kazerouni, A.; Aghdam, E. K.; Velichko, Y.; Bagci, U.; and Merhof, D. CoRR, abs/2309.00121. 2023.
Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation [link]Paper   doi   link   bibtex  
Self-supervised Semantic Segmentation: Consistency over Transformation. Karimijafarbigloo, S.; Azad, R.; Kazerouni, A.; Velichko, Y.; Bagci, U.; and Merhof, D. CoRR, abs/2309.00143. 2023.
Self-supervised Semantic Segmentation: Consistency over Transformation [link]Paper   doi   link   bibtex  
Radiomics Boosts Deep Learning Model for IPMN Classification. Yao, L.; Zhang, Z.; Demir, U.; Keles, E.; Vendrami, C.; Agarunov, E.; Bolan, C. W.; Schoots, I.; Bruno, M. J.; Keswani, R.; Miller, F. H.; Gonda, T.; Yazici, C.; Tirkes, T.; Wallace, M. B.; Spampinato, C.; and Bagci, U. CoRR, abs/2309.05857. 2023.
Radiomics Boosts Deep Learning Model for IPMN Classification [link]Paper   doi   link   bibtex  
Domain Generalization with Fourier Transform and Soft Thresholding. Pan, H.; Wang, B.; Zhan, Z.; Zhu, X.; Jha, D.; Çetin, A. E.; Spampinato, C.; and Bagci, U. CoRR, abs/2309.09866. 2023.
Domain Generalization with Fourier Transform and Soft Thresholding [link]Paper   doi   link   bibtex  
A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network. Familiar, A. M.; Kazerooni, A. F.; Anderson, H.; Lubneuski, A.; Viswanathan, K.; Breslow, R.; Khalili, N.; Bagheri, S.; Haldar, D.; Kim, M. C.; Arif, S.; Madhogarhia, R.; Nguyen, T. Q.; Frenkel, E. A.; Helili, Z.; Harrison, J.; Farahani, K.; Linguraru, M. G.; Bagci, U.; Velichko, Y.; Stevens, J.; Leary, S. E. S.; Lober, R. M.; Campion, S.; Smith, A. A.; Morinigo, D.; Rood, B.; Diamond, K.; Pollack, I. F.; Williams, M.; Vossough, A.; Ware, J. B.; Müller, S.; Storm, P. B.; Heath, A. P.; Waanders, A. J.; Lilly, J. V.; Mason, J. L.; Resnick, A. C.; and Nabavizadeh, A. CoRR, abs/2310.01413. 2023.
A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network [link]Paper   doi   link   bibtex  
A Non-monotonic Smooth Activation Function. Biswas, K.; Karri, M.; and Bagci, U. CoRR, abs/2310.10126. 2023.
A Non-monotonic Smooth Activation Function [link]Paper   doi   link   bibtex  
EMIT-Diff: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model. Zhang, Z.; Yao, L.; Wang, B.; Jha, D.; Keles, E.; Medetalibeyoglu, A.; and Bagci, U. CoRR, abs/2310.12868. 2023.
EMIT-Diff: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model [link]Paper   doi   link   bibtex  
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation. Gorade, V.; Mittal, S.; Jha, D.; and Bagci, U. CoRR, abs/2310.17764. 2023.
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation [link]Paper   doi   link   bibtex  
INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings. Kazerouni, A.; Azad, R.; Hosseini, A.; Merhof, D.; and Bagci, U. CoRR, abs/2310.18846. 2023.
INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings [link]Paper   doi   link   bibtex  
HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling. Bozorgpour, A.; Azad, B.; Azad, R.; Velichko, Y.; Bagci, U.; and Merhof, D. CoRR, abs/2311.12486. 2023.
HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling [link]Paper   doi   link   bibtex  
Leveraging Unlabeled Data for 3D Medical Image Segmentation through Self-Supervised Contrastive Learning. Karimijafarbigloo, S.; Azad, R.; Velichko, Y.; Bagci, U.; and Merhof, D. CoRR, abs/2311.12617. 2023.
Leveraging Unlabeled Data for 3D Medical Image Segmentation through Self-Supervised Contrastive Learning [link]Paper   doi   link   bibtex  
FuseNet: Self-Supervised Dual-Path Network for Medical Image Segmentation. Kazerouni, A.; Karimijafarbigloo, S.; Azad, R.; Velichko, Y.; Bagci, U.; and Merhof, D. CoRR, abs/2311.13069. 2023.
FuseNet: Self-Supervised Dual-Path Network for Medical Image Segmentation [link]Paper   doi   link   bibtex  
Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor Segmentation. Gorade, V.; Mittal, S.; Jha, D.; and Bagci, U. CoRR, abs/2311.16700. 2023.
Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor Segmentation [link]Paper   doi   link   bibtex  
PGS: Pose-Guided Supervision for Mitigating Clothes-Changing in Person Re-Identification. Trinh, Q.; Bui, N.; Hoang, D.; Thi, P. V.; Nguyen, H.; Jha, D.; Bagci, U.; Le, N.; and Tran, M. CoRR, abs/2312.05634. 2023.
PGS: Pose-Guided Supervision for Mitigating Clothes-Changing in Person Re-Identification [link]Paper   doi   link   bibtex  
Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans. Biswas, K.; Jha, D.; Tomar, N. K.; Durak, G.; Medetalibeyoglu, A.; Antalek, M.; Velichko, Y.; Ladner, D. P.; Borhani, A.; and Bagci, U. CoRR, abs/2312.11480. 2023.
Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans [link]Paper   doi   link   bibtex  
AI Powered Road Network Prediction with Multi-Modal Data. Gengeç, N. E.; Tari, E.; and Bagci, U. CoRR, abs/2312.17040. 2023.
AI Powered Road Network Prediction with Multi-Modal Data [link]Paper   doi   link   bibtex  
  2022 (31)
Extended Reality (XR) for Condition Assessment of Civil Engineering Structures: A Literature Review. Catbas, F. N.; Luleci, F.; Zakaria, M.; Bagci, U.; LaViola, J. J.; Cruz-Neira, C.; and Reiners, D. Sensors, 22(23): 9560. 2022.
Extended Reality (XR) for Condition Assessment of Civil Engineering Structures: A Literature Review [link]Paper   doi   link   bibtex  
Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI. Anwar, S. M.; Irmakci, I.; Torigian, D. A.; Jambawalikar, S.; Papadakis, G. Z.; Akgun, C.; Ellermann, J.; Akçakaya, M.; and Bagci, U. J. Signal Process. Syst., 94(5): 497–510. 2022.
Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI [link]Paper   doi   link   bibtex  
Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network. Tomar, N. K.; Srivastava, A.; Bagci, U.; and Jha, D. In 35th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2022, Shenzen, China, July 21-23, 2022, pages 317–322, 2022.
Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network [link]Paper   doi   link   bibtex  
Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network. Srivastava, A.; Tomar, N. K.; Bagci, U.; and Jha, D. In 35th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2022, Shenzen, China, July 21-23, 2022, pages 323–328, 2022.
Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network [link]Paper   doi   link   bibtex  
Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images. Salanitri, F. P.; Bellitto, G.; Palazzo, S.; Irmakci, I.; Wallace, M. B.; Bolan, C. W.; Engels, M.; Hoogenboom, S.; Aldinucci, M.; Bagci, U.; Giordano, D.; and Spampinato, C. In 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2022, Glasgow, Scotland, United Kingdom, July 11-15, 2022, pages 475–479, 2022.
Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images [link]Paper   doi   link   bibtex  
Multi-Contrast MRI Segmentation Trained on Synthetic Images. Irmakci, I.; Unel, Z. E.; Ikizler-Cinbis, N.; and Bagci, U. In 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2022, Glasgow, Scotland, United Kingdom, July 11-15, 2022, pages 5030–5034, 2022.
Multi-Contrast MRI Segmentation Trained on Synthetic Images [link]Paper   doi   link   bibtex  
Transformer Based Generative Adversarial Network for Liver Segmentation. Demir, U.; Zhang, Z.; Wang, B.; Antalek, M.; Keles, E.; Jha, D.; Borhani, A.; Ladner, D. P.; and Bagci, U. In Image Analysis and Processing. ICIAP 2022 Workshops - ICIAP International Workshops, Lecce, Italy, May 23-27, 2022, Revised Selected Papers, Part II, pages 340–347, 2022.
Transformer Based Generative Adversarial Network for Liver Segmentation [link]Paper   doi   link   bibtex  
Video Analytics in Elite Soccer: A Distributed Computing Perspective. Jha, D.; Rauniyar, A.; Johansen, H. D.; Johansen, D.; Riegler, M. A.; Halvorsen, P.; and Bagci, U. In 12th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2022, Trondheim, Norway, June 20-23, 2022, pages 221–225, 2022.
Video Analytics in Elite Soccer: A Distributed Computing Perspective [link]Paper   doi   link   bibtex  
Detecting COVID-19 from respiratory sound recordings with transformers. Aytekin, I.; Dalmaz, O.; Ankishan, H.; Saritas, E. U.; Bagci, U.; Çukur, T.; and Celik, H. In Medical Imaging 2022: Computer-Aided Diagnosis, San Diego, CA, USA, February 20-24, 2022 / online, March 21-27, 2022, 2022.
Detecting COVID-19 from respiratory sound recordings with transformers [link]Paper   doi   link   bibtex  
Multi-scale Fusion Methodologies for Head and Neck Tumor Segmentation. Srivastava, A.; Jha, D.; Aydogan, B.; Abazeed, M. E.; and Bagci, U. In Head and Neck Tumor Segmentation and Outcome Prediction - Third Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings, pages 107–113, 2022.
Multi-scale Fusion Methodologies for Head and Neck Tumor Segmentation [link]Paper   doi   link   bibtex  
TGANet: Text-Guided Attention for Improved Polyp Segmentation. Tomar, N. K.; Jha, D.; Bagci, U.; and Ali, S. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 - 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part III, pages 151–160, 2022.
TGANet: Text-Guided Attention for Improved Polyp Segmentation [link]Paper   doi   link   bibtex  
Dynamic Linear Transformer for 3D Biomedical Image Segmentation. Zhang, Z.; and Bagci, U. In Machine Learning in Medical Imaging - 13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings, pages 171–180, 2022.
Dynamic Linear Transformer for 3D Biomedical Image Segmentation [link]Paper   doi   link   bibtex  
Enhancing organ at risk segmentation with improved deep neural networks. Isler, I.; Lisle, C.; Rineer, J.; Kelly, P.; Turgut, D.; Ricci, J.; and Bagci, U. In Medical Imaging 2022: Image Processing, San Diego, CA, USA, February 20-24, 2022 / Online, March 21-27, 2022, 2022.
Enhancing organ at risk segmentation with improved deep neural networks [link]Paper   doi   link   bibtex  
Medical Image Learning with Limited and Noisy Data - First International Workshop, MILLanD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Zamzmi, G.; Antani, S. K.; Bagci, U.; Linguraru, M. G.; Rajaraman, S.; and Xue, Z., editors. Volume 13559, of Lecture Notes in Computer Science.Springer. 2022.
Medical Image Learning with Limited and Noisy Data - First International Workshop, MILLanD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings [link]Paper   doi   link   bibtex  
Enhancing Organ at Risk Segmentation with Improved Deep Neural Networks. Isler, I.; Lisle, C.; Rineer, J.; Kelly, P.; Turgut, D.; Ricci, J.; and Bagci, U. CoRR, abs/2202.01866. 2022.
Enhancing Organ at Risk Segmentation with Improved Deep Neural Networks [link]Paper   link   bibtex  
Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem. Marvasti, A. E.; Marvasti, E. E.; and Bagci, U. CoRR, abs/2203.12145. 2022.
Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem [link]Paper   doi   link   bibtex  
TGANet: Text-guided attention for improved polyp segmentation. Tomar, N. K.; Jha, D.; Bagci, U.; and Ali, S. CoRR, abs/2205.04280. 2022.
TGANet: Text-guided attention for improved polyp segmentation [link]Paper   doi   link   bibtex  
Transformer based Generative Adversarial Network for Liver Segmentation. Demir, U.; Zhang, Z.; Wang, B.; Antalek, M.; Keles, E.; Jha, D.; Borhani, A.; Ladner, D. P.; and Bagci, U. CoRR, abs/2205.10663. 2022.
Transformer based Generative Adversarial Network for Liver Segmentation [link]Paper   doi   link   bibtex  
Dynamic Linear Transformer for 3D Biomedical Image Segmentation. Zhang, Z.; and Bagci, U. CoRR, abs/2206.00771. 2022.
Dynamic Linear Transformer for 3D Biomedical Image Segmentation [link]Paper   doi   link   bibtex  
Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network. Tomar, N. K.; Srivastava, A.; Bagci, U.; and Jha, D. CoRR, abs/2206.06264. 2022.
Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network [link]Paper   doi   link   bibtex  
Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network. Srivastava, A.; Tomar, N. K.; Bagci, U.; and Jha, D. CoRR, abs/2206.08298. 2022.
Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network [link]Paper   doi   link   bibtex  
TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation. Tomar, N. K.; Shergill, A.; Rieders, B.; Bagci, U.; and Jha, D. CoRR, abs/2206.08985. 2022.
TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation [link]Paper   doi   link   bibtex  
Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images. Salanitri, F. P.; Bellitto, G.; Palazzo, S.; Irmakci, I.; Wallace, M. B.; Bolan, C. W.; Engels, M.; Hoogenboom, S.; Aldinucci, M.; Bagci, U.; Giordano, D.; and Spampinato, C. CoRR, abs/2206.10531. 2022.
Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images [link]Paper   doi   link   bibtex  
Video Analytics in Elite Soccer: A Distributed Computing Perspective. Jha, D.; Rauniyar, A.; Johansen, H. D.; Johansen, D.; Riegler, M. A.; Halvorsen, P.; and Bagci, U. CoRR, abs/2206.11335. 2022.
Video Analytics in Elite Soccer: A Distributed Computing Perspective [link]Paper   doi   link   bibtex  
Multi-Contrast MRI Segmentation Trained on Synthetic Images. Irmakci, I.; Unel, Z. E.; Ikizler-Cinbis, N.; and Bagci, U. CoRR, abs/2207.02469. 2022.
Multi-Contrast MRI Segmentation Trained on Synthetic Images [link]Paper   doi   link   bibtex  
COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers. Aytekin, I.; Dalmaz, O.; Gonc, K.; Ankishan, H.; Saritas, E. U.; Bagci, U.; Celik, H.; and Çukur, T. CoRR, abs/2207.09529. 2022.
COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers [link]Paper   doi   link   bibtex  
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions. Rauniyar, A.; Hagos, D. H.; Jha, D.; Håkegård, J. E.; Bagci, U.; Rawat, D. B.; and Vlassov, V. CoRR, abs/2208.03392. 2022.
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions [link]Paper   doi   link   bibtex  
An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation. Srivastava, A.; Jha, D.; Keles, E.; Aydogan, B.; Abazeed, M.; and Bagci, U. CoRR, abs/2208.07417. 2022.
An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation [link]Paper   doi   link   bibtex  
DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation. Tomar, N. K.; Jha, D.; and Bagci, U. CoRR, abs/2210.13595. 2022.
DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation [link]Paper   doi   link   bibtex  
Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation. Srivastava, A.; Jha, D.; Aydogan, B.; Abazeed, M. E.; and Bagci, U. CoRR, abs/2210.16704. 2022.
Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation [link]Paper   doi   link   bibtex  
Domain Generalization with Correlated Style Uncertainty. Zhang, Z.; Wang, B.; Jha, D.; Demir, U.; and Bagci, U. CoRR, abs/2212.09950. 2022.
Domain Generalization with Correlated Style Uncertainty [link]Paper   doi   link   bibtex  
  2021 (12)
Deep Learning Based Staging of Bone Lesions From Computed Tomography Scans. Masoudi, S.; Mehralivand, S.; Harmon, S. A.; Lay, N.; Lindenberg, L.; Mena, E.; Pinto, P. A.; Citrin, D. E.; Gulley, J. L.; Wood, B. J.; Dahut, W. L.; Madan, R. A.; Bagci, U.; Choyke, P. L.; and Turkbey, B. IEEE Access, 9: 87531–87542. 2021.
Deep Learning Based Staging of Bone Lesions From Computed Tomography Scans [link]Paper   doi   link   bibtex  
Capsules for biomedical image segmentation. LaLonde, R.; Xu, Z.; Irmakci, I.; Jain, S.; and Bagci, U. Medical Image Anal., 68: 101889. 2021.
Capsules for biomedical image segmentation [link]Paper   doi   link   bibtex  
Maximum Probability Theorem: A Framework for Probabilistic Machine Learning. Marvasti, A. E.; Marvasti, E. E.; Bagci, U.; and Foroosh, H. IEEE Trans. Artif. Intell., 2(3): 214–227. 2021.
Maximum Probability Theorem: A Framework for Probabilistic Machine Learning [link]Paper   doi   link   bibtex  
Predicting RF Heating of Conductive Leads During Magnetic Resonance Imaging at 1.5 T: A Machine Learning Approach\(^\mbox*\). Zheng, C.; Chen, X.; Nguyen, B. T.; Sanpitak, P.; Vu, J.; Bagci, U.; and Golestanirad, L. In 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2021, Mexico, November 1-5, 2021, pages 4204–4208, 2021.
Predicting RF Heating of Conductive Leads During Magnetic Resonance Imaging at 1.5 T: A Machine Learning Approach\(^\mbox*\) [link]Paper   doi   link   bibtex  
Interpretable Deep Model For Predicting Gene-Addicted Non-Small-Cell Lung Cancer In Ct Scans. Pino, C.; Palazzo, S.; Trenta, F.; Cordero, F.; Bagci, U.; Rundo, F.; Battiato, S.; Giordano, D.; Aldinucci, M.; and Spampinato, C. In 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021, Nice, France, April 13-16, 2021, pages 891–894, 2021.
Interpretable Deep Model For Predicting Gene-Addicted Non-Small-Cell Lung Cancer In Ct Scans [link]Paper   doi   link   bibtex  
No-Reference Image Quality Assessment Of T2-Weighted Magnetic Resonance Images In Prostate Cancer Patients. Masoudi, S.; Harmon, S. A.; Mehralivand, S.; Lay, N.; Bagci, U.; Wood, B. J.; Pinto, P. A.; Choyke, P. L.; and Turkbey, B. In 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021, Nice, France, April 13-16, 2021, pages 1201–1205, 2021.
No-Reference Image Quality Assessment Of T2-Weighted Magnetic Resonance Images In Prostate Cancer Patients [link]Paper   doi   link   bibtex  
Hierarchical 3D Feature Learning forPancreas Segmentation. Salanitri, F. P.; Bellitto, G.; Irmakci, I.; Palazzo, S.; Bagci, U.; and Spampinato, C. In Machine Learning in Medical Imaging - 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings, pages 238–247, 2021.
Hierarchical 3D Feature Learning forPancreas Segmentation [link]Paper   doi   link   bibtex  
Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis. Demir, U.; Irmakci, I.; Keles, E.; Topcu, A.; Xu, Z.; Spampinato, C.; Jambawalikar, S.; Turkbey, E.; Turkbey, B.; and Bagci, U. In Machine Learning in Medical Imaging - 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings, pages 396–405, 2021.
Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis [link]Paper   doi   link   bibtex  
Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data. Nawaz, A.; Anwar, S. M.; Liaqat, R.; Iqbal, J.; Bagci, U.; and Majid, M. CoRR, abs/2101.02876. 2021.
Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data [link]Paper   link   bibtex  
Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis. Demir, U.; Irmakci, I.; Keles, E.; Topcu, A.; Xu, Z.; Spampinato, C.; Jambawalikar, S.; Turkbey, E.; Turkbey, B.; and Bagci, U. CoRR, abs/2104.02869. 2021.
Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis [link]Paper   link   bibtex  
Deformable Capsules for Object Detection. LaLonde, R.; Khosravan, N.; and Bagci, U. CoRR, abs/2104.05031. 2021.
Deformable Capsules for Object Detection [link]Paper   link   bibtex  
Hierarchical 3D Feature Learning for Pancreas Segmentation. Salanitri, F. P.; Bellitto, G.; Irmakci, I.; Palazzo, S.; Bagci, U.; and Spampinato, C. CoRR, abs/2109.01667. 2021.
Hierarchical 3D Feature Learning for Pancreas Segmentation [link]Paper   link   bibtex  
  2020 (19)
Analysis of Video Retinal Angiography With Deep Learning and Eulerian Magnification. Laha, S.; LaLonde, R.; Carmack, A. E.; Foroosh, H.; Olson, J. C.; Shaikh, S.; and Bagci, U. Frontiers Comput. Sci., 2: 24. 2020.
Analysis of Video Retinal Angiography With Deep Learning and Eulerian Magnification [link]Paper   doi   link   bibtex  
EEG Based Classification of Long-Term Stress Using Psychological Labeling. Saeed, S. M. U.; Anwar, S. M.; Khalid, H.; Majid, M.; and Bagci, U. Sensors, 20(7): 1886. 2020.
EEG Based Classification of Long-Term Stress Using Psychological Labeling [link]Paper   doi   link   bibtex  
Instance-Level Microtubule Tracking. Masoudi, S.; Razi, A.; Wright, C. H. G.; Gatlin, J. C.; and Bagci, U. IEEE Trans. Medical Imaging, 39(6): 2061–2075. 2020.
Instance-Level Microtubule Tracking [link]Paper   doi   link   bibtex  
Adipose Tissue Segmentation in Unlabeled Abdomen MRI using Cross Modality Domain Adaptation. Masoudi, S.; Anwar, S. M.; Harmon, S. A.; Choyke, P. L.; Turkbey, B.; and Bagci, U. In 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2020, Montreal, QC, Canada, July 20-24, 2020, pages 1624–1628, 2020.
Adipose Tissue Segmentation in Unlabeled Abdomen MRI using Cross Modality Domain Adaptation [link]Paper   doi   link   bibtex  
Variational Capsule Encoder. RaviPrakash, H.; Anwar, S. M.; and Bagci, U. In 25th International Conference on Pattern Recognition, ICPR 2020, Virtual Event / Milan, Italy, January 10-15, 2021, pages 5820–5827, 2020.
Variational Capsule Encoder [link]Paper   doi   link   bibtex  
Deep Recurrent-Convolutional Model for Automated Segmentation of Craniomaxillofacial CT Scans. Murabito, F.; Palazzo, S.; Salanitri, F. P.; Rundo, F.; Bagci, U.; Giordano, D.; Leonardi, R.; and Spampinato, C. In 25th International Conference on Pattern Recognition, ICPR 2020, Virtual Event / Milan, Italy, January 10-15, 2021, pages 9062–9067, 2020.
Deep Recurrent-Convolutional Model for Automated Segmentation of Craniomaxillofacial CT Scans [link]Paper   doi   link   bibtex  
Deep Multi-stage Model for Automated Landmarking of Craniomaxillofacial CT Scans. Palazzo, S.; Bellitto, G.; Prezzavento, L.; Rundo, F.; Bagci, U.; Giordano, D.; Leonardi, R.; and Spampinato, C. In 25th International Conference on Pattern Recognition, ICPR 2020, Virtual Event / Milan, Italy, January 10-15, 2021, pages 9982–9987, 2020.
Deep Multi-stage Model for Automated Landmarking of Craniomaxillofacial CT Scans [link]Paper   doi   link   bibtex  
Diagnosing Colorectal Polyps in the Wild with Capsule Networks. LaLonde, R.; Kandel, P.; Spampinato, C.; Wallace, M. B.; and Bagci, U. In 17th IEEE International Symposium on Biomedical Imaging, ISBI 2020, Iowa City, IA, USA, April 3-7, 2020, pages 1086–1090, 2020.
Diagnosing Colorectal Polyps in the Wild with Capsule Networks [link]Paper   doi   link   bibtex  
State-of-the-Art in Brain Tumor Segmentation and Current Challenges. Yousaf, S.; RaviPrakash, H.; Anwar, S. M.; Sohail, N.; and Bagci, U. In Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology - Third International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings, pages 189–198, 2020.
State-of-the-Art in Brain Tumor Segmentation and Current Challenges [link]Paper   doi   link   bibtex  
Overall Survival Prediction in Gliomas Using Region-Specific Radiomic Features. Shaheen, A.; Burigat, S.; Bagci, U.; and Mohy-ud-Din, H. In Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology - Third International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings, pages 259–267, 2020.
Overall Survival Prediction in Gliomas Using Region-Specific Radiomic Features [link]Paper   doi   link   bibtex  
Brain Tumor Survival Prediction Using Radiomics Features. Yousaf, S.; Anwar, S. M.; RaviPrakash, H.; and Bagci, U. In Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology - Third International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings, pages 284–293, 2020.
Brain Tumor Survival Prediction Using Radiomics Features [link]Paper   doi   link   bibtex  
Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses. LaLonde, R.; Torigian, D. A.; and Bagci, U. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2020 - 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part I, pages 294–304, 2020.
Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses [link]Paper   doi   link   bibtex  
Diagnosing Colorectal Polyps in the Wild with Capsule Networks. LaLonde, R.; Kandel, P.; Spampinato, C.; Wallace, M. B.; and Bagci, U. CoRR, abs/2001.03305. 2020.
Diagnosing Colorectal Polyps in the Wild with Capsule Networks [link]Paper   link   bibtex  
Deep Learning for Musculoskeletal Image Analysis. Irmakci, I.; Anwar, S. M.; Torigian, D. A.; and Bagci, U. CoRR, abs/2003.00541. 2020.
Deep Learning for Musculoskeletal Image Analysis [link]Paper   link   bibtex  
Capsules for Biomedical Image Segmentation. LaLonde, R.; Xu, Z.; Jain, S.; and Bagci, U. CoRR, abs/2004.04736. 2020.
Capsules for Biomedical Image Segmentation [link]Paper   link   bibtex  
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset. Desai, A. D.; Calivá, F.; Iriondo, C.; Khosravan, N.; Mortazi, A.; Jambawalikar, S.; Torigian, D. A.; Ellermann, J.; Akçakaya, M.; Bagci, U.; Tibrewala, R.; Flament, I.; O'Brien, M.; Majumdar, S.; Perslev, M.; Pai, A.; Igel, C.; Dam, E. B.; Gaj, S.; Yang, M.; Nakamura, K.; Li, X.; Deniz, C. M.; Juras, V.; Regatte, R.; Gold, G.; Hargreaves, B. A.; Pedoia, V.; and Chaudhari, A. S. CoRR, abs/2004.14003. 2020.
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset [link]Paper   link   bibtex  
Adipose Tissue Segmentation in Unlabeled Abdomen MRI using Cross Modality Domain Adaptation. Masoudi, S.; Anwar, S. M.; Harmon, S. A.; Choyke, P. L.; Turkbey, B.; and Bagci, U. CoRR, abs/2005.05761. 2020.
Adipose Tissue Segmentation in Unlabeled Abdomen MRI using Cross Modality Domain Adaptation [link]Paper   link   bibtex  
Brain Tumor Survival Prediction using Radiomics Features. Yousaf, S.; Anwar, S. M.; RaviPrakash, H.; and Bagci, U. CoRR, abs/2009.02903. 2020.
Brain Tumor Survival Prediction using Radiomics Features [link]Paper   link   bibtex  
Variational Capsule Encoder. RaviPrakash, H.; Anwar, S. M.; and Bagci, U. CoRR, abs/2010.09102. 2020.
Variational Capsule Encoder [link]Paper   link   bibtex  
  2019 (29)
Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks. Stember, J. N.; Celik, H.; Krupinski, E. A.; Chang, P. D.; Mutasa, S.; Wood, B. J.; Lignelli, A.; Moonis, G.; Schwartz, L. H.; Jambawalikar, S.; and Bagci, U. J. Digit. Imaging, 32(4): 597–604. 2019.
Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks [link]Paper   doi   link   bibtex  
A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning. Khosravan, N.; Celik, H.; Turkbey, B.; Jones, E. C.; Wood, B. J.; and Bagci, U. Medical Image Anal., 51: 101–115. 2019.
A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning [link]Paper   doi   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, Ö.; Bian, C.; Yang, X.; Heng, P.; Mortazi, A.; Bagci, U.; Yang, G.; Sun, C.; Galisot, G.; Ramel, J.; and Yang, G. Medical Image Anal., 58. 2019.
Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge [link]Paper   doi   link   bibtex  
A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation. Irmakci, I.; Hussein, S.; Savran, A.; Kalyani, R. R.; Reiter, D.; Chia, C. W.; Fishbein, K. W.; Spencer, R. G. S.; Ferrucci, L.; and Bagci, U. IEEE Trans. Biomed. Eng., 66(4): 1069–1081. 2019.
A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation [link]Paper   doi   link   bibtex  
Deep Geodesic Learning for Segmentation and Anatomical Landmarking. Torosdagli, N.; Liberton, D. K.; Verma, P.; Sincan, M.; Lee, J. S.; and Bagci, U. IEEE Trans. Medical Imaging, 38(4): 919–931. 2019.
Deep Geodesic Learning for Segmentation and Anatomical Landmarking [link]Paper   doi   link   bibtex  
Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches. Hussein, S.; Kandel, P.; Bolan, C. W.; Wallace, M. B.; and Bagci, U. IEEE Trans. Medical Imaging, 38(8): 1777–1787. 2019.
Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches [link]Paper   doi   link   bibtex  
RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge. Bogunovic, H.; Venhuizen, F. G.; Klimscha, S.; Apostolopoulos, S.; Bab-Hadiashar, A.; Bagci, U.; Beg, M. F.; Bekalo, L.; Chen, Q.; Ciller, C.; Gopinath, K.; Gostar, A. K.; Jeon, K.; Ji, Z.; Kang, S. H.; Koozekanani, D. D.; Lu, D.; Morley, D.; Parhi, K. K.; Park, H. S.; Rashno, A.; Sarunic, M.; Shaikh, S.; Sivaswamy, J.; Tennakoon, R. B.; Yadav, S.; Zanet, S. D.; Waldstein, S. M.; Gerendas, B. S.; Klaver, C.; Sánchez, C. I.; and Schmidt-Erfurth, U. IEEE Trans. Medical Imaging, 38(8): 1858–1874. 2019.
RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge [link]Paper   doi   link   bibtex  
Deep Learning for Musculoskeletal Image Analysis. Irmakci, I.; Anwar, S. M.; Torigian, D. A.; and Bagci, U. In 53rd Asilomar Conference on Signals, Systems, and Computers, ACSCC 2019, Pacific Grove, CA, USA, November 3-6, 2019, pages 1481–1485, 2019.
Deep Learning for Musculoskeletal Image Analysis [link]Paper   doi   link   bibtex  
Emotion Classification in Response to Tactile Enhanced Multimedia using Frequency Domain Features of Brain Signals. Raheel, A.; Majid, M.; Anwar, S. M.; and Bagci, U. In 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019, Berlin, Germany, July 23-27, 2019, pages 1201–1204, 2019.
Emotion Classification in Response to Tactile Enhanced Multimedia using Frequency Domain Features of Brain Signals [link]Paper   doi   link   bibtex  
Classification of Perceived Human Stress using Physiological Signals. Arsalan, A.; Majid, M.; Anwar, S. M.; and Bagci, U. In 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019, Berlin, Germany, July 23-27, 2019, pages 1247–1250, 2019.
Classification of Perceived Human Stress using Physiological Signals [link]Paper   doi   link   bibtex  
A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology. Anwar, S. M.; Altaf, T.; Rafique, K.; RaviPrakash, H.; Mohy-ud-Din, H.; and Bagci, U. In Radiomics and Radiogenomics in Neuro-oncology - First International Workshop, RNO-AI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, Proceedings, pages 24–35, 2019.
A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology [link]Paper   doi   link   bibtex  
Cross-Modality Knowledge Transfer for Prostate Segmentation from CT Scans. Liu, Y.; Khosravan, N.; Liu, Y.; Stember, J. N.; Shoag, J.; Bagci, U.; and Jambawalikar, S. In Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data - First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings, pages 63–71, 2019.
Cross-Modality Knowledge Transfer for Prostate Segmentation from CT Scans [link]Paper   doi   link   bibtex  
PAN: Projective Adversarial Network for Medical Image Segmentation. Khosravan, N.; Mortazi, A.; Wallace, M. B.; and Bagci, U. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2019 - 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part VI, pages 68–76, 2019.
PAN: Projective Adversarial Network for Medical Image Segmentation [link]Paper   doi   link   bibtex  
INN: Inflated Neural Networks for IPMN Diagnosis. LaLonde, R.; Tanner, I.; Nikiforaki, K.; Papadakis, G. Z.; Kandel, P.; Bolan, C. W.; Wallace, M. B.; and Bagci, U. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2019 - 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part V, pages 101–109, 2019.
INN: Inflated Neural Networks for IPMN Diagnosis [link]Paper   doi   link   bibtex  
Weakly Supervised Segmentation by a Deep Geodesic Prior. Mortazi, A.; Khosravan, N.; Torigian, D. A.; Kurugol, S.; and Bagci, U. In Machine Learning in Medical Imaging - 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings, pages 238–246, 2019.
Weakly Supervised Segmentation by a Deep Geodesic Prior [link]Paper   doi   link   bibtex  
Deep Learning for Functional Brain Connectivity: Are We There Yet?. RaviPrakash, H.; Watane, A.; Jambawalikar, S.; and Bagci, U. In Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics, pages 347–365. 2019.
Deep Learning for Functional Brain Connectivity: Are We There Yet? [link]Paper   doi   link   bibtex  
Gold Standard for Epilepsy/Tumor Surgery Coupled with Deep Learning Offers Independence to a Promising Functional Mapping Modality. Korostenskaja, M.; Raviprakash, H.; Bagci, U.; Lee, K. H.; Chen, P. C.; Kapeller, C.; Salinas, C.; Westerveld, M.; Ralescu, A. L.; Xiang, J.; Baumgartner, J.; Elsayed, M.; and Castillo, E. M. In Brain-Computer Interface Research - A State-of-the-Art Summary 7, pages 11–29. 2019.
Gold Standard for Epilepsy/Tumor Surgery Coupled with Deep Learning Offers Independence to a Promising Functional Mapping Modality [link]Paper   doi   link   bibtex  
Instance-Level Microtubule Segmentation Using Recurrent Attention. Masoudi, S.; Razi, A.; Wright, C. H. G.; Gatlin, J. C.; and Bagci, U. CoRR, abs/1901.06006. 2019.
Instance-Level Microtubule Segmentation Using Recurrent Attention [link]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, Ö.; Bian, C.; Yang, X.; Heng, P.; Mortazi, A.; Bagci, U.; Yang, G.; Sun, C.; Galisot, G.; Ramel, J.; Brouard, T.; Tong, Q.; Si, W.; Liao, X.; Zeng, G.; Shi, Z.; Zheng, G.; Wang, C.; MacGillivray, T. J.; Newby, D. E.; Rhode, K. S.; Ourselin, S.; Mohiaddin, R.; Keegan, J.; Firmin, D. N.; and Yang, G. CoRR, abs/1902.07880. 2019.
Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge [link]Paper   link   bibtex  
Relational Reasoning Network (RRN) for Anatomical Landmarking. Torosdagli, N.; McIntosh, M.; Liberton, D. K.; Verma, P.; Sincan, M.; Han, W. W.; Lee, J. S.; and Bagci, U. CoRR, abs/1904.04354. 2019.
Relational Reasoning Network (RRN) for Anatomical Landmarking [link]Paper   link   bibtex  
Classification of Perceived Human Stress using Physiological Signals. Arsalan, A.; Majid, M.; Anwar, S. M.; and Bagci, U. CoRR, abs/1905.06384. 2019.
Classification of Perceived Human Stress using Physiological Signals [link]Paper   link   bibtex  
Emotion Classification in Response to Tactile Enhanced Multimedia using Frequency Domain Features of Brain Signals. Raheel, A.; Majid, M.; Anwar, S. M.; and Bagci, U. CoRR, abs/1905.10423. 2019.
Emotion Classification in Response to Tactile Enhanced Multimedia using Frequency Domain Features of Brain Signals [link]Paper   link   bibtex  
PAN: Projective Adversarial Network for Medical Image Segmentation. Khosravan, N.; Mortazi, A.; Wallace, M. B.; and Bagci, U. CoRR, abs/1906.04378. 2019.
PAN: Projective Adversarial Network for Medical Image Segmentation [link]Paper   link   bibtex  
INN: Inflated Neural Networks for IPMN Diagnosis. LaLonde, R.; Tanner, I.; Nikiforaki, K.; Papadakis, G. Z.; Kandel, P.; Bolan, C. W.; Wallace, M. B.; and Bagci, U. CoRR, abs/1907.00437. 2019.
INN: Inflated Neural Networks for IPMN Diagnosis [link]Paper   link   bibtex  
Electroencephalography based Classification of Long-term Stress using Psychological Labeling. Saeed, S. M. U.; Anwar, S. M.; Khalid, H.; Majid, M.; and Bagci, U. CoRR, abs/1907.07671. 2019.
Electroencephalography based Classification of Long-term Stress using Psychological Labeling [link]Paper   link   bibtex  
Weakly Supervised Segmentation by A Deep Geodesic Prior. Mortazi, A.; Khosravan, N.; Torigian, D. A.; Kurugol, S.; and Bagci, U. CoRR, abs/1908.06498. 2019.
Weakly Supervised Segmentation by A Deep Geodesic Prior [link]Paper   link   bibtex  
Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans. Liu, Y.; Khosravan, N.; Liu, Y.; Stember, J. N.; Shoag, J.; Bagci, U.; and Jambawalikar, S. CoRR, abs/1908.10208. 2019.
Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans [link]Paper   link   bibtex  
Encoding High-Level Visual Attributes in Capsules for Explainable Medical Diagnoses. LaLonde, R.; Torigian, D. A.; and Bagci, U. CoRR, abs/1909.05926. 2019.
Encoding High-Level Visual Attributes in Capsules for Explainable Medical Diagnoses [link]Paper   link   bibtex  
A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology. Anwar, S. M.; Altaf, T.; Rafique, K.; RaviPrakash, H.; Mohy-ud-Din, H.; and Bagci, U. CoRR, abs/1910.07470. 2019.
A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology [link]Paper   link   bibtex  
  2018 (17)
Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Gao, M.; Bagci, U.; Lu, L.; Wu, A.; Buty, M.; Shin, H.; Roth, H.; Papadakis, G. Z.; Depeursinge, A.; Summers, R. M.; Xu, Z.; and Mollura, D. J. Comput. methods Biomech. Biomed. Eng. Imaging Vis., 6(1): 1–6. 2018.
Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks [link]Paper   doi   link   bibtex  
Joint solution for PET image segmentation, denoising, and partial volume correction. Xu, Z.; Gao, M.; Papadakis, G. Z.; Luna, B.; Jain, S.; Mollura, D. J.; and Bagci, U. Medical Image Anal., 46: 229–243. 2018.
Joint solution for PET image segmentation, denoising, and partial volume correction [link]Paper   doi   link   bibtex  
Semi-Supervised Multi-Task Learning for Lung Cancer Diagnosis. Khosravan, N.; and Bagci, U. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018, Honolulu, HI, USA, July 18-21, 2018, pages 710–713, 2018.
Semi-Supervised Multi-Task Learning for Lung Cancer Diagnosis [link]Paper   doi   link   bibtex  
How to fool radiologists with generative adversarial networks? A visual turing test for lung cancer diagnosis. Chuquicusma, M. J. M.; Hussein, S.; Burt, J.; and Bagci, U. In 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Washington, DC, USA, April 4-7, 2018, pages 240–244, 2018.
How to fool radiologists with generative adversarial networks? A visual turing test for lung cancer diagnosis [link]Paper   doi   link   bibtex  
Deep multi-modal classification of intraductal papillary mucinous neoplasms (IPMN) with canonical correlation analysis. Hussein, S.; Kandel, P.; Corral, J. E.; Bolan, C. W.; Wallace, M. B.; and Bagci, U. In 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Washington, DC, USA, April 4-7, 2018, pages 800–804, 2018.
Deep multi-modal classification of intraductal papillary mucinous neoplasms (IPMN) with canonical correlation analysis [link]Paper   doi   link   bibtex  
Automatically Designing CNN Architectures for Medical Image Segmentation. Mortazi, A.; and Bagci, U. In Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings, pages 98–106, 2018.
Automatically Designing CNN Architectures for Medical Image Segmentation [link]Paper   doi   link   bibtex  
S4ND: Single-Shot Single-Scale Lung Nodule Detection. Khosravan, N.; and Bagci, U. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2018 - 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II, pages 794–802, 2018.
S4ND: Single-Shot Single-Scale Lung Nodule Detection [link]Paper   doi   link   bibtex  
Supervised and Unsupervised Tumor Characterization in the Deep Learning Era. Hussein, S.; Chuquicusma, M. M. J.; Kandel, P.; Bolan, C. W.; Wallace, M. B.; and Bagci, U. CoRR, abs/1801.03230. 2018.
Supervised and Unsupervised Tumor Characterization in the Deep Learning Era [link]Paper   link   bibtex  
Semi-supervised multi-task learning for lung cancer diagnosis. Khosravan, N.; and Bagci, U. CoRR, abs/1802.06181. 2018.
Semi-supervised multi-task learning for lung cancer diagnosis [link]Paper   link   bibtex  
A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning. Khosravan, N.; Celik, H.; Turkbey, B.; Jones, E. C.; Wood, B. J.; and Bagci, U. CoRR, abs/1802.06260. 2018.
A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning [link]Paper   link   bibtex  
Capsules for Object Segmentation. LaLonde, R.; and Bagci, U. CoRR, abs/1804.04241. 2018.
Capsules for Object Segmentation [link]Paper   link   bibtex  
S4ND: Single-Shot Single-Scale Lung Nodule Detection. Khosravan, N.; and Bagci, U. CoRR, abs/1805.02279. 2018.
S4ND: Single-Shot Single-Scale Lung Nodule Detection [link]Paper   link   bibtex  
End to End Brain Fiber Orientation Estimation using Deep Learning. Puttashamachar, N.; and Bagci, U. CoRR, abs/1806.03969. 2018.
End to End Brain Fiber Orientation Estimation using Deep Learning [link]Paper   link   bibtex  
Automatically Designing CNN Architectures for Medical Image Segmentation. Mortazi, A.; and Bagci, U. CoRR, abs/1807.07663. 2018.
Automatically Designing CNN Architectures for Medical Image Segmentation [link]Paper   link   bibtex  
Deep Geodesic Learning for Segmentation and Anatomical Landmarking. Torosdagli, N.; Liberton, D. K.; Verma, P.; Sincan, M.; Lee, J. S.; and Bagci, U. CoRR, abs/1810.04021. 2018.
Deep Geodesic Learning for Segmentation and Anatomical Landmarking [link]Paper   link   bibtex  
A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation. Irmakci, I.; Hussein, S.; Savran, A.; Kalyani, R. R.; Reiter, D.; Chia, C. W.; Fishbein, K. W.; Spencer, R. G. S.; Ferrucci, L.; and Bagci, U. CoRR, abs/1810.06071. 2018.
A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation [link]Paper   link   bibtex  
Artificial Intelligence Assisted Infrastructure Assessment Using Mixed Reality Systems. Karaaslan, E.; Bagci, U.; and Catbas, F. N. CoRR, abs/1812.05659. 2018.
Artificial Intelligence Assisted Infrastructure Assessment Using Mixed Reality Systems [link]Paper   link   bibtex  
  2017 (18)
CorteXpert: A model-based method for automatic renal cortex segmentation. Xiang, D.; Bagci, U.; Jin, C.; Shi, F.; Zhu, W.; Yao, J.; Sonka, M.; and Chen, X. Medical Image Anal., 42: 257–273. 2017.
CorteXpert: A model-based method for automatic renal cortex segmentation [link]Paper   doi   link   bibtex  
Single-Channel Sparse Non-Negative Blind Source Separation Method for Automatic 3-D Delineation of Lung Tumor in PET Images. Kopriva, I.; Ju, W.; Zhang, B.; Shi, F.; Xiang, D.; Yu, K.; Wang, X.; Bagci, U.; and Chen, X. IEEE J. Biomed. Health Informatics, 21(6): 1656–1666. 2017.
Single-Channel Sparse Non-Negative Blind Source Separation Method for Automatic 3-D Delineation of Lung Tumor in PET Images [link]Paper   doi   link   bibtex  
Automatic Segmentation and Quantification of White and Brown Adipose Tissues from PET/CT Scans. Hussein, S.; Green, A.; Watane, A.; Reiter, D.; Chen, X.; Papadakis, G. Z.; Wood, B. J.; Cypess, A.; Osman, M. M.; and Bagci, U. IEEE Trans. Medical Imaging, 36(3): 734–744. 2017.
Automatic Segmentation and Quantification of White and Brown Adipose Tissues from PET/CT Scans [link]Paper   doi   link   bibtex  
Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning. Hussein, S.; Cao, K.; Song, Q.; and Bagci, U. In Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Boone, NC, USA, June 25-30, 2017, Proceedings, pages 249–260, 2017.
Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning [link]Paper   doi   link   bibtex  
TumorNet: Lung nodule characterization using multi-view Convolutional Neural Network with Gaussian Process. Hussein, S.; Gillies, R. J.; Cao, K.; Song, Q.; and Bagci, U. In 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017, Melbourne, Australia, April 18-21, 2017, pages 1007–1010, 2017.
TumorNet: Lung nodule characterization using multi-view Convolutional Neural Network with Gaussian Process [link]Paper   doi   link   bibtex  
Robust and fully automated segmentation of mandible from CT scans. Torosdagli, N.; Liberton, D. K.; Verma, P.; Sincan, M.; Lee, J.; Pattanaik, S. N.; and Bagci, U. In 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017, Melbourne, Australia, April 18-21, 2017, pages 1209–1212, 2017.
Robust and fully automated segmentation of mandible from CT scans [link]Paper   doi   link   bibtex  
Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT. Mortazi, A.; Burt, J.; and Bagci, U. In 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, pages 199–206, 2017.
Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT [link]Paper   doi   link   bibtex  
CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI Using Multi-view CNN. Mortazi, A.; Karim, R.; Rhode, K. S.; Burt, J.; and Bagci, U. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2017 - 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II, pages 377–385, 2017.
CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI Using Multi-view CNN [link]Paper   doi   link   bibtex  
Automatic response assessment in regions of language cortex in epilepsy patients using ECoG-based functional mapping and machine learning. RaviPrakash, H.; Korostenskaja, M.; Lee, K.; Baumgartner, J.; Castillo, E.; and Bagci, U. In 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, Banff, AB, Canada, October 5-8, 2017, pages 519–524, 2017.
Automatic response assessment in regions of language cortex in epilepsy patients using ECoG-based functional mapping and machine learning [link]Paper   doi   link   bibtex  
Robust and fully automated segmentation of mandible from CT scans. Torosdagli, N.; Liberton, D. K.; Verma, P.; Sincan, M.; Lee, J.; Pattanaik, S. N.; and Bagci, U. CoRR, abs/1702.07059. 2017.
Robust and fully automated segmentation of mandible from CT scans [link]Paper   link   bibtex  
TumorNet: Lung Nodule Characterization Using Multi-View Convolutional Neural Network with Gaussian Process. Hussein, S.; Gillies, R. J.; Cao, K.; Song, Q.; and Bagci, U. CoRR, abs/1703.00645. 2017.
TumorNet: Lung Nodule Characterization Using Multi-View Convolutional Neural Network with Gaussian Process [link]Paper   link   bibtex  
Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning. Hussein, S.; Cao, K.; Song, Q.; and Bagci, U. CoRR, abs/1704.08797. 2017.
Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning [link]Paper   link   bibtex  
CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI Using Multi-View CNN. Mortazi, A.; Karim, R.; Rhode, K. S.; Burt, J.; and Bagci, U. CoRR, abs/1705.06333. 2017.
CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI Using Multi-View CNN [link]Paper   link   bibtex  
Automatic Response Assessment in Regions of Language Cortex in Epilepsy Patients Using ECoG-based Functional Mapping and Machine Learning. RaviPrakash, H.; Korostenskaja, M.; Castillo, E.; Lee, K.; Baumgartner, J.; and Bagci, U. CoRR, abs/1706.01380. 2017.
Automatic Response Assessment in Regions of Language Cortex in Epilepsy Patients Using ECoG-based Functional Mapping and Machine Learning [link]Paper   link   bibtex  
Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT. Mortazi, A.; Burt, J.; and Bagci, U. CoRR, abs/1708.00983. 2017.
Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT [link]Paper   link   bibtex  
Simultaneous Detection and Quantification of Retinal Fluid with Deep Learning. Morley, D.; Foroosh, H.; Shaikh, S.; and Bagci, U. CoRR, abs/1708.05464. 2017.
Simultaneous Detection and Quantification of Retinal Fluid with Deep Learning [link]Paper   link   bibtex  
How to Fool Radiologists with Generative Adversarial Networks? A Visual Turing Test for Lung Cancer Diagnosis. Chuquicusma, M. J. M.; Hussein, S.; Burt, J.; and Bagci, U. CoRR, abs/1710.09762. 2017.
How to Fool Radiologists with Generative Adversarial Networks? A Visual Turing Test for Lung Cancer Diagnosis [link]Paper   link   bibtex  
Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis. Hussein, S.; Kandel, P.; Corral, J. E.; Bolan, C. W.; Wallace, M. B.; and Bagci, U. CoRR, abs/1710.09779. 2017.
Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis [link]Paper   link   bibtex  
  2016 (6)
Computer-Aided Detection (CADx) for Plastic Deformation Fractures in Pediatric Forearm. Zhou, Y.; Teomete, U.; Dandin, O.; Osman, O.; Dandinoglu, T.; Bagci, U.; and Zhao, W. Comput. Biol. Medicine, 78: 120–125. 2016.
Computer-Aided Detection (CADx) for Plastic Deformation Fractures in Pediatric Forearm [link]Paper   doi   link   bibtex  
Atlas-based rib-bone detection in chest X-rays. Candemir, S.; Jaeger, S.; Antani, S. K.; Bagci, U.; Folio, L. R.; Xu, Z.; and Thoma, G. R. Comput. Medical Imaging Graph., 51: 32–39. 2016.
Atlas-based rib-bone detection in chest X-rays [link]Paper   doi   link   bibtex  
Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation. Khosravan, N.; Celik, H.; Turkbey, B.; Cheng, R.; McCreedy, E. S.; McAuliffe, M. J.; Bednarova, S.; Jones, E. C.; Chen, X.; Choyke, P. L.; Wood, B. J.; and Bagci, U. In Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers, pages 94–104, 2016.
Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation [link]Paper   doi   link   bibtex  
Characterization of Lung Nodule Malignancy Using Hybrid Shape and Appearance Features. Buty, M.; Xu, Z.; Gao, M.; Bagci, U.; Wu, A.; and Mollura, D. J. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part I, pages 662–670, 2016.
Characterization of Lung Nodule Malignancy Using Hybrid Shape and Appearance Features [link]Paper   doi   link   bibtex  
Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation. Khosravan, N.; Celik, H.; Turkbey, B.; Cheng, R.; McCreedy, E. S.; McAuliffe, M. J.; Bednarova, S.; Jones, E. C.; Chen, X.; Choyke, P. L.; Wood, B. J.; and Bagci, U. CoRR, abs/1608.03235. 2016.
Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation [link]Paper   link   bibtex  
Characterization of Lung Nodule Malignancy using Hybrid Shape and Appearance Features. Buty, M.; Xu, Z.; Gao, M.; Bagci, U.; Wu, A.; and Mollura, D. J. CoRR, abs/1609.06668. 2016.
Characterization of Lung Nodule Malignancy using Hybrid Shape and Appearance Features [link]Paper   link   bibtex  
  2015 (4)
A hybrid method for airway segmentation and automated measurement of bronchial wall thickness on CT. Xu, Z.; Bagci, U.; Foster, B.; Mansoor, A.; Udupa, J. K.; and Mollura, D. J. Medical Image Anal., 24(1): 1–17. 2015.
A hybrid method for airway segmentation and automated measurement of bronchial wall thickness on CT [link]Paper   doi   link   bibtex  
Correction to "A Generic Approach to Pathological Lung Segmentation". Mansoor, A.; Bagci, U.; Xu, Z.; Foster, B.; Olivier, K. N.; Elinoff, J. M.; Suffredini, A. F.; Udupa, J. K.; and Mollura, D. J. IEEE Trans. Medical Imaging, 34(1): 354. 2015.
Correction to "A Generic Approach to Pathological Lung Segmentation" [link]Paper   doi   link   bibtex  
Highly precise partial volume correction for PET images: An iterative approach via shape consistency. Xu, Z.; Bagci, U.; Gao, M.; and Mollura, D. J. In 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, NY, USA, April 16-19, 2015, pages 1196–1199, 2015.
Highly precise partial volume correction for PET images: An iterative approach via shape consistency [link]Paper   doi   link   bibtex  
Context Driven Label Fusion for segmentation of Subcutaneous and Visceral Fat in CT Volumes. Hussein, S.; Green, A.; Watane, A.; Papadakis, G. Z.; Osman, M. M.; and Bagci, U. CoRR, abs/1512.04958. 2015.
Context Driven Label Fusion for segmentation of Subcutaneous and Visceral Fat in CT Volumes [link]Paper   link   bibtex  
  2014 (12)
A review on segmentation of positron emission tomography images. Foster, B.; Bagci, U.; Mansoor, A.; Xu, Z.; and Mollura, D. J. Comput. Biol. Medicine, 50: 76–96. 2014.
A review on segmentation of positron emission tomography images [link]Paper   doi   link   bibtex  
Segmentation of PET Images for Computer-Aided Functional Quantification of Tuberculosis in Small Animal Models. Foster, B.; Bagci, U.; Xu, Z.; Dey, B.; Luna, B.; Bishai, W.; Jain, S.; and Mollura, D. J. IEEE Trans. Biomed. Eng., 61(3): 711–724. 2014.
Segmentation of PET Images for Computer-Aided Functional Quantification of Tuberculosis in Small Animal Models [link]Paper   doi   link   bibtex  
A Generic Approach to Pathological Lung Segmentation. Mansoor, A.; Bagci, U.; Xu, Z.; Foster, B.; Olivier, K. N.; Elinoff, J. M.; Suffredini, A. F.; Udupa, J. K.; and Mollura, D. J. IEEE Trans. Medical Imaging, 33(12): 2293–2310. 2014.
A Generic Approach to Pathological Lung Segmentation [link]Paper   doi   link   bibtex  
CIDI-lung-seg: A single-click annotation tool for automatic delineation of lungs from CT scans. Mansoor, A.; Bagci, U.; Foster, B.; Xu, Z.; Douglas, D.; Solomon, J. M.; Udupa, J. K.; and Mollura, D. J. In 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Chicago, IL, USA, August 26-30, 2014, pages 1087–1090, 2014.
CIDI-lung-seg: A single-click annotation tool for automatic delineation of lungs from CT scans [link]Paper   doi   link   bibtex  
Efficient ribcage segmentation from CT scans using shape features. Xu, Z.; Bagci, U.; Jonsson, C. B.; Jain, S.; and Mollura, D. J. In 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Chicago, IL, USA, August 26-30, 2014, pages 2899–2902, 2014.
Efficient ribcage segmentation from CT scans using shape features [link]Paper   doi   link   bibtex  
Near-optimal keypoint sampling for fast pathological lung segmentation. Mansoor, A.; Bagci, U.; and Mollura, D. J. In 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Chicago, IL, USA, August 26-30, 2014, pages 6032–6035, 2014.
Near-optimal keypoint sampling for fast pathological lung segmentation [link]Paper   doi   link   bibtex  
Accurate and efficient separation of left and right lungs from 3D CT scans: A generic hysteresis approach. Xu, Z.; Bagci, U.; Jonsson, C. B.; Jain, S.; and Mollura, D. J. In 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Chicago, IL, USA, August 26-30, 2014, pages 6036–6039, 2014.
Accurate and efficient separation of left and right lungs from 3D CT scans: A generic hysteresis approach [link]Paper   doi   link   bibtex  
Optimally Stabilized PET Image Denoising Using Trilateral Filtering. Mansoor, A.; Bagci, U.; and Mollura, D. J. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014 - 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part I, pages 130–137, 2014.
Optimally Stabilized PET Image Denoising Using Trilateral Filtering [link]Paper   doi   link   bibtex  
Segmentation Based Denoising of PET Images: An Iterative Approach via Regional Means and Affinity Propagation. Xu, Z.; Bagci, U.; Seidel, J.; Thomasson, D.; Solomon, J. M.; and Mollura, D. J. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014 - 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part I, pages 698–705, 2014.
Segmentation Based Denoising of PET Images: An Iterative Approach via Regional Means and Affinity Propagation [link]Paper   doi   link   bibtex  
CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans. Mansoor, A.; Bagci, U.; Foster, B.; Xu, Z.; Douglas, D.; Solomon, J. M.; Udupa, J. K.; and Mollura, D. J. CoRR, abs/1407.3176. 2014.
CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans [link]Paper   link   bibtex  
Near-optimal Keypoint Sampling for Fast Pathological Lung Segmentation. Mansoor, A.; Bagci, U.; and Mollura, D. J. CoRR, abs/1407.3179. 2014.
Near-optimal Keypoint Sampling for Fast Pathological Lung Segmentation [link]Paper   link   bibtex  
Optimally Stabilized PET Image Denoising Using Trilateral Filtering. Mansoor, A.; Bagci, U.; and Mollura, D. J. CoRR, abs/1407.3193. 2014.
Optimally Stabilized PET Image Denoising Using Trilateral Filtering [link]Paper   link   bibtex  
  2013 (7)
Synergistic combination of clinical and imaging features predicts abnormal imaging patterns of pulmonary infections. Bagci, U.; Miller-Jaster, K.; Olivier, K. N.; Yao, J.; and Mollura, D. J. Comput. Biol. Medicine, 43(9): 1241–1251. 2013.
Synergistic combination of clinical and imaging features predicts abnormal imaging patterns of pulmonary infections [link]Paper   doi   link   bibtex  
Joint segmentation of anatomical and functional images: Applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images. Bagci, U.; Udupa, J. K.; Mendhiratta, N.; Foster, B.; Xu, Z.; Yao, J.; Chen, X.; and Mollura, D. J. Medical Image Anal., 17(8): 929–945. 2013.
Joint segmentation of anatomical and functional images: Applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images [link]Paper   doi   link   bibtex  
Introducing Willmore Flow Into Level Set Segmentation of Spinal Vertebrae. Lim, P. H.; Bagci, U.; and Bai, L. IEEE Trans. Biomed. Eng., 60(1): 115–122. 2013.
Introducing Willmore Flow Into Level Set Segmentation of Spinal Vertebrae [link]Paper   doi   link   bibtex  
A hybrid multi-scale approach to automatic airway tree segmentation from CT scans. Xu, Z.; Bagci, U.; Foster, B.; and Mollura, D. J. In 10th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013, 7-11 April, 2013, San Francisco, CA, USA, Proceedings, pages 1308–1311, 2013.
A hybrid multi-scale approach to automatic airway tree segmentation from CT scans [link]Paper   doi   link   bibtex  
Robust segmentation and accurate target definition for positron emission tomography images using Affinity Propagation. Foster, B.; Bagci, U.; Luna, B.; Dey, B.; Bishai, W.; Jain, S.; Xu, Z.; and Mollura, D. J. In 10th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013, 7-11 April, 2013, San Francisco, CA, USA, Proceedings, pages 1461–1464, 2013.
Robust segmentation and accurate target definition for positron emission tomography images using Affinity Propagation [link]Paper   doi   link   bibtex  
Denoising PET Images Using Singular Value Thresholding and Stein's Unbiased Risk Estimate. Bagci, U.; and Mollura, D. J. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2013 - 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part III, pages 115–122, 2013.
Denoising PET Images Using Singular Value Thresholding and Stein's Unbiased Risk Estimate [link]Paper   doi   link   bibtex  
Spatially Constrained Random Walk Approach for Accurate Estimation of Airway Wall Surfaces. Xu, Z.; Bagci, U.; Foster, B.; Mansoor, A.; and Mollura, D. J. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2013 - 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part II, pages 559–566, 2013.
Spatially Constrained Random Walk Approach for Accurate Estimation of Airway Wall Surfaces [link]Paper   doi   link   bibtex  
  2012 (9)
Computer-assisted detection of infectious lung diseases: A review. Bagci, U.; Bray, M.; Caban, J.; Yao, J.; and Mollura, D. J. Comput. Medical Imaging Graph., 36(1): 72–84. 2012.
Computer-assisted detection of infectious lung diseases: A review [link]Paper   doi   link   bibtex  
Corrigendum to "Computer-assisted detection of infectious lung diseases: A review" [Comput. Med. Imag. Graph. 36(2012) 72-84]. Bagci, U.; Bray, M.; Caban, J.; Yao, J.; and Mollura, D. J. Comput. Medical Imaging Graph., 36(2): 169. 2012.
Corrigendum to "Computer-assisted detection of infectious lung diseases: A review" [Comput. Med. Imag. Graph. 36(2012) 72-84] [link]Paper   doi   link   bibtex  
Automatic Detection and Quantification of Tree-in-Bud (TIB) Opacities From CT Scans. Bagci, U.; Yao, J.; Wu, A.; Caban, J.; Palmore, T. N.; Suffredini, A. F.; Aras, O.; and Mollura, D. J. IEEE Trans. Biomed. Eng., 59(6): 1620–1632. 2012.
Automatic Detection and Quantification of Tree-in-Bud (TIB) Opacities From CT Scans [link]Paper   doi   link   bibtex  
Medical Image Segmentation by Combining Graph Cuts and Oriented Active Appearance Models. Chen, X.; Udupa, J. K.; Bagci, U.; Zhuge, Y.; and Yao, J. IEEE Trans. Image Process., 21(4): 2035–2046. 2012.
Medical Image Segmentation by Combining Graph Cuts and Oriented Active Appearance Models [link]Paper   doi   link   bibtex  
Hierarchical Scale-Based Multiobject Recognition of 3-D Anatomical Structures. Bagci, U.; Chen, X.; and Udupa, J. K. IEEE Trans. Medical Imaging, 31(3): 777–789. 2012.
Hierarchical Scale-Based Multiobject Recognition of 3-D Anatomical Structures [link]Paper   doi   link   bibtex  
Characterizing non-linear dependencies among pairs of clinical variables and imaging data. Caban, J. J.; Bagci, U.; Mehari, A.; Alam, S.; Fontana, J. R.; Kato, G. J.; and Mollura, D. J. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012, San Diego, CA, USA, August 28 - September 1, 2012, pages 2700–2703, 2012.
Characterizing non-linear dependencies among pairs of clinical variables and imaging data [link]Paper   doi   link   bibtex  
Automatic quantification of Tree-in-Bud patterns from CT scans. Bagci, U.; Miller-Jaster, K.; Yao, J.; Wu, A.; Caban, J.; Olivier, K. N.; Aras, O.; and Mollura, D. J. In 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012, May 2-5, 2012, Barcelona, Spain, Proceedings, pages 1459–1462, 2012.
Automatic quantification of Tree-in-Bud patterns from CT scans [link]Paper   doi   link   bibtex  
A novel spinal vertebrae segmentation framework combining geometric flow and shape prior with level set method. Lim, P. H.; Bagci, U.; Aras, O.; Wang, Y.; and Bai, L. In 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012, May 2-5, 2012, Barcelona, Spain, Proceedings, pages 1703–1706, 2012.
A novel spinal vertebrae segmentation framework combining geometric flow and shape prior with level set method [link]Paper   doi   link   bibtex  
Co-segmentation of Functional and Anatomical Images. Bagci, U.; Udupa, J. K.; Yao, J.; and Mollura, D. J. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2012 - 15th International Conference, Nice, France, October 1-5, 2012, Proceedings, Part III, pages 459–467, 2012.
Co-segmentation of Functional and Anatomical Images [link]Paper   doi   link   bibtex  
  2011 (8)
A New Prior Shape Model for Level Set Segmentation. Lim, P. H.; Bagci, U.; and Bai, L. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 16th Iberoamerican Congress, CIARP 2011, Pucón, Chile, November 15-18, 2011. Proceedings, pages 125–132, 2011.
A New Prior Shape Model for Level Set Segmentation [link]Paper   doi   link   bibtex  
Automatic detection of tree-in-bud patterns for computer assisted diagnosis of respiratory tract infections. Bagci, U.; Yao, J.; Caban, J.; Palmore, T. N.; Suffredini, A. F.; and Mollura, D. J. In 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011, Boston, MA, USA, August 30 - Sept. 3, 2011, pages 5096–5099, 2011.
Automatic detection of tree-in-bud patterns for computer assisted diagnosis of respiratory tract infections [link]Paper   doi   link   bibtex  
Monitoring pulmonary fibrosis by fusing clinical, physiological, and computed tomography features. Caban, J. J.; Yao, J.; Bagci, U.; and Mollura, D. J. In 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011, Boston, MA, USA, August 30 - Sept. 3, 2011, pages 6216–6219, 2011.
Monitoring pulmonary fibrosis by fusing clinical, physiological, and computed tomography features [link]Paper   doi   link   bibtex  
A graph-theoretic approach for segmentation of PET images. Bagci, U.; Yao, J.; Caban, J.; Turkbey, E.; Aras, O.; and Mollura, D. J. In 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011, Boston, MA, USA, August 30 - Sept. 3, 2011, pages 8479–8482, 2011.
A graph-theoretic approach for segmentation of PET images [link]Paper   doi   link   bibtex  
Learning Shape and Texture Characteristics of CT Tree-in-Bud Opacities for CAD Systems. Bagci, U.; Yao, J.; Caban, J.; Suffredini, A. F.; Palmore, T. N.; and Mollura, D. J. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011 - 14th International Conference, Toronto, Canada, September 18-22, 2011, Proceedings, Part III, pages 215–222, 2011.
Learning Shape and Texture Characteristics of CT Tree-in-Bud Opacities for CAD Systems [link]Paper   doi   link   bibtex  
Intensity non-standardness affects computer recognition of anatomical structures. Bagci, U.; Udupa, J. K.; and Chen, X. In Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, Lake Buena Vista (Orlando), Florida, United States, 12-17 February 2011, pages 79642M, 2011.
Intensity non-standardness affects computer recognition of anatomical structures [link]Paper   doi   link   bibtex  
Orientation estimation of anatomical structures in medical images for object recognition. Bagci, U.; Udupa, J. K.; and Chen, X. In Medical Imaging 2011: Image Processing, Lake Buena Vista, Florida, USA, February 14-16, 2011, pages 79622L, 2011.
Orientation estimation of anatomical structures in medical images for object recognition [link]Paper   doi   link   bibtex  
Learning Shape and Texture Characteristics of CT Tree-in-Bud Opacities for CAD Systems. Bagci, U.; Yao, J.; Caban, J.; Suffredini, A. F.; Palmore, T. N.; and Mollura, D. J. CoRR, abs/1106.5186. 2011.
Learning Shape and Texture Characteristics of CT Tree-in-Bud Opacities for CAD Systems [link]Paper   link   bibtex  
  2010 (8)
The role of intensity standardization in medical image registration. Bagci, U.; Udupa, J. K.; and Bai, L. Pattern Recognit. Lett., 31(4): 315–323. 2010.
The role of intensity standardization in medical image registration [link]Paper   doi   link   bibtex  
Automatic Best Reference Slice Selection for Smooth Volume Reconstruction of a Mouse Brain From Histological Images. Bagci, U.; and Bai, L. IEEE Trans. Medical Imaging, 29(9): 1688–1696. 2010.
Automatic Best Reference Slice Selection for Smooth Volume Reconstruction of a Mouse Brain From Histological Images [link]Paper   doi   link   bibtex  
3D automatic anatomy segmentation based on graph cut-oriented active appearance models. Chen, X.; Yao, J.; Zhuge, Y.; and Bagci, U. In Proceedings of the International Conference on Image Processing, ICIP 2010, September 26-29, Hong Kong, China, pages 3653–3656, 2010.
3D automatic anatomy segmentation based on graph cut-oriented active appearance models [link]Paper   doi   link   bibtex  
The influence of intensity standardization on medical image registration. Bagci, U.; Udupa, J. K.; and Bai, L. In Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, San Diego, California, United States, 13-18 February 2010, pages 76251X, 2010.
The influence of intensity standardization on medical image registration [link]Paper   doi   link   bibtex  
3D automatic anatomy recognition based on iterative graph-cut-ASM. Chen, X.; Udupa, J. K.; Bagci, U.; Alavi, A.; and Torigian, D. A. In Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, San Diego, California, United States, 13-18 February 2010, pages 76251T, 2010.
3D automatic anatomy recognition based on iterative graph-cut-ASM [link]Paper   doi   link   bibtex  
Ball-scale based hierarchical multi-object recognition in 3D medical images. Bagci, U.; Udupa, J. K.; and Chen, X. In Medical Imaging 2010: Image Processing, San Diego, California, USA, February 14-16, 2010, pages 762345, 2010.
Ball-scale based hierarchical multi-object recognition in 3D medical images [link]Paper   doi   link   bibtex  
The Influence of Intensity Standardization on Medical Image Registration. Bagci, U.; Udupa, J. K.; and Bai, L. CoRR, abs/1002.1285. 2010.
The Influence of Intensity Standardization on Medical Image Registration [link]Paper   link   bibtex  
Ball-Scale Based Hierarchical Multi-Object Recognition in 3D Medical Images. Bagci, U.; Udupa, J. K.; and Chen, X. CoRR, abs/1002.1288. 2010.
Ball-Scale Based Hierarchical Multi-Object Recognition in 3D Medical Images [link]Paper   link   bibtex  
  2009 (5)
Multiresolution Elastic Medical Image Registration in Standard Intensity Scale. Bagci, U.; and Bai, L. CoRR, abs/0907.2075. 2009.
Multiresolution Elastic Medical Image Registration in Standard Intensity Scale [link]Paper   link   bibtex  
Registration of Standardized Histological Images in Feature Space. Bagci, U.; and Bai, L. CoRR, abs/0907.3209. 2009.
Registration of Standardized Histological Images in Feature Space [link]Paper   link   bibtex  
Fully Automatic 3D Reconstruction of Histological Images. Bagci, U.; and Bai, L. CoRR, abs/0907.3215. 2009.
Fully Automatic 3D Reconstruction of Histological Images [link]Paper   link   bibtex  
Parallel AdaBoost Algorithm for Gabor Wavelet Selection in Face Recognition. Bagci, U.; and Bai, L. CoRR, abs/0907.3218. 2009.
Parallel AdaBoost Algorithm for Gabor Wavelet Selection in Face Recognition [link]Paper   link   bibtex  
Inter Genre Similarity Modelling For Automatic Music Genre Classification. Bagci, U.; and Erzin, E. CoRR, abs/0907.3220. 2009.
Inter Genre Similarity Modelling For Automatic Music Genre Classification [link]Paper   link   bibtex  
  2008 (3)
Parallel AdaBoost algorithm for Gabor wavelet selection in face recognition. Bagci, U.; and Bai, L. In Proceedings of the International Conference on Image Processing, ICIP 2008, October 12-15, 2008, San Diego, California, USA, pages 1640–1643, 2008.
Parallel AdaBoost algorithm for Gabor wavelet selection in face recognition [link]Paper   doi   link   bibtex  
Fully automatic 3D reconstruction of histological images. Bagci, U.; and Bai, L. In Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France, May 14-17, 2008, pages 991–994, 2008.
Fully automatic 3D reconstruction of histological images [link]Paper   doi   link   bibtex  
Registration of standardized histological images in feature space. Bagci, U.; and Bai, L. In Medical Imaging 2008: Image Processing, San Diego, California, United States, 16-21 February 2008, pages 69142V, 2008.
Registration of standardized histological images in feature space [link]Paper   doi   link   bibtex  
  2007 (2)
Automatic Classification of Musical Genres Using Inter-Genre Similarity. Bagci, U.; and Erzin, E. IEEE Signal Process. Lett., 14(8): 521–524. 2007.
Automatic Classification of Musical Genres Using Inter-Genre Similarity [link]Paper   doi   link   bibtex  
Multiresolution Elastic Medical Image Registration in Standard Intensity Scale. Bagci, U.; and Bai, L. In SIBGRAPI 2007, Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing, Belo Horizonte, Brazil, 7-10 October 2007, pages 305–312, 2007.
Multiresolution Elastic Medical Image Registration in Standard Intensity Scale [link]Paper   doi   link   bibtex  
  2005 (1)
Boosting Classifiers for Music Genre Classification. Bagci, U.; and Erzin, E. In Computer and Information Sciences - ISCIS 2005, 20th International Symposium, Istanbul, Turkey, October 26-28, 2005, Proceedings, pages 575–584, 2005.
Boosting Classifiers for Music Genre Classification [link]Paper   doi   link   bibtex