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\n \n \n Fix it now\n

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\n  \n 2019\n \n \n (5)\n \n \n
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\n \n \n
\n \n\n \n \n \n \n \n \n Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification.\n \n \n \n \n\n\n \n Xue, Y.; Zhou, Q.; Ye, J.; Long, L R.; Antani, S.; Cornwell, C.; Xue, Z.; and Huang, X.\n\n\n \n\n\n\n In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 387–396, 2019. Springer\n \n\n\n\n
\n\n\n\n \n \n \"Synthetic paper\n  \n \n \n \"Synthetic link\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{xue2019synthetic,\r\n  title={Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification},\r\n  author={Xue, Yuan and Zhou, Qianying and Ye, Jiarong and Long, L Rodney and Antani, Sameer and Cornwell, Carl and Xue, Zhiyun and Huang, Xiaolei},\r\n  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},\r\n  pages={387--396},\r\n  year={2019},\r\n  organization={Springer},\r\n  url_Paper = {https://faculty.ist.psu.edu/suh972/MICCAI2019_SyntheticFiltering.pdf},\r\n  url_Link = {https://link.springer.com/chapter/10.1007/978-3-030-32239-7_43}\r\n}
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\n \n\n \n \n \n \n \n \n Improved Disease Classification in Chest X-Rays with Transferred Features from Report Generation.\n \n \n \n \n\n\n \n Xue, Y.; and Huang, X.\n\n\n \n\n\n\n In International Conference on Information Processing in Medical Imaging, pages 125–138, 2019. Springer\n \n\n\n\n
\n\n\n\n \n \n \"Improved link\n  \n \n \n \"Improved paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{xue2019improved,\r\n  title={Improved Disease Classification in Chest X-Rays with Transferred Features from Report Generation},\r\n  author={Xue, Yuan and Huang, Xiaolei},\r\n  booktitle={International Conference on Information Processing in Medical Imaging},\r\n  pages={125--138},\r\n  year={2019},\r\n  url_Link={https://link.springer.com/chapter/10.1007/978-3-030-20351-1_10},\r\n  url_Paper={https://faculty.ist.psu.edu/suh972/Xue-IPMI2019.pdf},\r\n  organization={Springer}\r\n}
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\n \n\n \n \n \n \n \n \n Automated Tracking of Biopolymer Growth and Network Deformation with TSOAX.\n \n \n \n \n\n\n \n Xu, T.; Langouras, C.; Koudehi, M. A.; Vos, B. E; Wang, N.; Koenderink, G. H; Huang, X.; and Vavylonis, D.\n\n\n \n\n\n\n Scientific reports, 9(1): 1717. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"Automated link\n  \n \n \n \"Automated paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{xu2019automated,\r\n  title={Automated Tracking of Biopolymer Growth and Network Deformation with TSOAX},\r\n  author={Xu, Ting and Langouras, Christos and Koudehi, Maral Adeli and Vos, Bart E and Wang, Ning and Koenderink, Gijsje H and Huang, Xiaolei and Vavylonis, Dimitrios},\r\n  journal={Scientific reports},\r\n  volume={9},\r\n  number={1},\r\n  pages={1717},\r\n  url_Link = {https://www.nature.com/articles/s41598-018-37182-6},\r\n  url_Paper = {https://faculty.ist.psu.edu/suh972/TSOAX_2019.pdf},\r\n  year={2019},\r\n  publisher={Nature Publishing Group}\r\n}
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\n \n\n \n \n \n \n \n \n Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics.\n \n \n \n \n\n\n \n Shen, Z.; Wang, J.; Jiang, J.; Huang, S. X; Lin, Y.; Nan, C.; Chen, L.; and Shen, Y.\n\n\n \n\n\n\n Nature communications, 10(1): 1843. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"Phase-field link\n  \n \n \n \"Phase-field paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{shen2019phase,\r\n  title={Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics},\r\n  author={Shen, Zhong-Hui and Wang, Jian-Jun and Jiang, Jian-Yong and Huang, Sharon X and Lin, Yuan-Hua and Nan, Ce-Wen and Chen, Long-Qing and Shen, Yang},\r\n  journal={Nature communications},\r\n  volume={10},\r\n  number={1},\r\n  pages={1843},\r\n  year={2019},\r\n  url_Link = {https://www.nature.com/articles/s41467-019-09874-8},\r\n  url_Paper = {https://faculty.ist.psu.edu/suh972/Shen-etal_NatureCommunications-19.pdf},\r\n  publisher={Nature Publishing Group}\r\n}
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\n \n\n \n \n \n \n \n \n Computer-Aided Diagnosis of Label-Free 3-D Optical Coherence Microscopy Images of Human Cervical Tissue.\n \n \n \n \n\n\n \n Ma, Y.; Xu, T.; Huang, X.; Wang, X.; Li, C.; Jerwick, J.; Ning, Y.; Zeng, X.; Wang, B.; Wang, Y.; and others\n\n\n \n\n\n\n IEEE Transactions on Biomedical Engineering. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"Computer-Aided link\n  \n \n \n \"Computer-Aided paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{ma2019computer,\r\n  title={Computer-Aided Diagnosis of Label-Free 3-D Optical Coherence Microscopy Images of Human Cervical Tissue},\r\n  author={Ma, Yutao and Xu, Tao and Huang, Xiaolei and Wang, Xiaofang and Li, Canyu and Jerwick, Jason and Ning, Yuan and Zeng, Xianxu and Wang, Baojin and Wang, Yihong and others},\r\n  journal={IEEE Transactions on Biomedical Engineering},\r\n  year={2019},\r\n  url_Link = {https://ieeexplore.ieee.org/document/8598821},\r\n  url_Paper = {https://faculty.ist.psu.edu/suh972/TBE_OCM_2019.pdf},\r\n  publisher={IEEE}\r\n}
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\n  \n 2018\n \n \n (6)\n \n \n
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\n \n\n \n \n \n \n \n \n Segan: Adversarial network with multi-scale l 1 loss for medical image segmentation.\n \n \n \n \n\n\n \n Xue, Y.; Xu, T.; Zhang, H.; Long, L R.; and Huang, X.\n\n\n \n\n\n\n Neuroinformatics, 16(3-4): 383–392. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"Segan: paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{xue2018segan,\r\n  title={Segan: Adversarial network with multi-scale l 1 loss for medical image segmentation},\r\n  author={Xue, Yuan and Xu, Tao and Zhang, Han and Long, L Rodney and Huang, Xiaolei},\r\n  journal={Neuroinformatics},\r\n  volume={16},\r\n  number={3-4},\r\n  pages={383--392},\r\n  url_Paper = {https://faculty.ist.psu.edu/suh972/Neuroinformatics_SegAN.pdf},\r\n  year={2018},\r\n  publisher={Springer}\r\n}
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\n \n\n \n \n \n \n \n \n Multimodal Recurrent Model with Attention for Automated Radiology Report Generation.\n \n \n \n \n\n\n \n Xue, Y.; Xu, T.; Long, L R.; Xue, Z.; Antani, S.; Thoma, G. R; and Huang, X.\n\n\n \n\n\n\n In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 457–466, 2018. Springer\n \n\n\n\n
\n\n\n\n \n \n \"Multimodal paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{xue2018multimodal,\r\n  title={Multimodal Recurrent Model with Attention for Automated Radiology Report Generation},\r\n  author={Xue, Yuan and Xu, Tao and Long, L Rodney and Xue, Zhiyun and Antani, Sameer and Thoma, George R and Huang, Xiaolei},\r\n  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},\r\n  pages={457--466},\r\n  year={2018},\r\n  url_Paper = {https://faculty.ist.psu.edu/suh972/Xue-MICCAI2018.pdf},\r\n  organization={Springer}\r\n}
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\n \n\n \n \n \n \n \n \n Adversarial learning with multi-scale loss for skin lesion segmentation.\n \n \n \n \n\n\n \n Xue, Y.; Xu, T.; and Huang, X.\n\n\n \n\n\n\n In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pages 859–863, 2018. IEEE\n \n\n\n\n
\n\n\n\n \n \n \"Adversarial link\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{xue2018adversarial,\r\n  title={Adversarial learning with multi-scale loss for skin lesion segmentation},\r\n  author={Xue, Yuan and Xu, Tao and Huang, Xiaolei},\r\n  booktitle={2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)},\r\n  pages={859--863},\r\n  url_Link = {https://ieeexplore.ieee.org/document/8363707},\r\n  year={2018},\r\n  organization={IEEE}\r\n}
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\n \n\n \n \n \n \n \n \n An efficient algorithm for dynamic MRI using low-rank and total variation regularizations.\n \n \n \n \n\n\n \n Yao, J.; Xu, Z.; Huang, X.; and Huang, J.\n\n\n \n\n\n\n Medical image analysis, 44: 14–27. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"An link\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{yao2018efficient,\r\n  title={An efficient algorithm for dynamic MRI using low-rank and total variation regularizations},\r\n  author={Yao, Jiawen and Xu, Zheng and Huang, Xiaolei and Huang, Junzhou},\r\n  journal={Medical image analysis},\r\n  volume={44},\r\n  pages={14--27},\r\n  url_Link = {https://www.ncbi.nlm.nih.gov/pubmed/29175383},\r\n  year={2018},\r\n  publisher={Elsevier}\r\n}
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\n \n\n \n \n \n \n \n \n Attngan: Fine-grained text to image generation with attentional generative adversarial networks.\n \n \n \n \n\n\n \n Xu, T.; Zhang, P.; Huang, Q.; Zhang, H.; Gan, Z.; Huang, X.; and He, X.\n\n\n \n\n\n\n In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1316–1324, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"Attngan: paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{xu2018attngan,\r\n  title={Attngan: Fine-grained text to image generation with attentional generative adversarial networks},\r\n  author={Xu, Tao and Zhang, Pengchuan and Huang, Qiuyuan and Zhang, Han and Gan, Zhe and Huang, Xiaolei and He, Xiaodong},\r\n  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},\r\n  pages={1316--1324},\r\n  url_Paper = {http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_AttnGAN_Fine-Grained_Text_CVPR_2018_paper.pdf},\r\n  year={2018}\r\n}
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\n \n\n \n \n \n \n \n \n Stackgan++: Realistic image synthesis with stacked generative adversarial networks.\n \n \n \n \n\n\n \n Zhang, H.; Xu, T.; Li, H.; Zhang, S.; Wang, X.; Huang, X.; and Metaxas, D. N\n\n\n \n\n\n\n IEEE transactions on pattern analysis and machine intelligence, 41(8): 1947–1962. 2018.\n \n\n\n\n
\n\n\n\n \n \n \"Stackgan++: link\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{zhang2018stackgan++,\r\n  title={Stackgan++: Realistic image synthesis with stacked generative adversarial networks},\r\n  author={Zhang, Han and Xu, Tao and Li, Hongsheng and Zhang, Shaoting and Wang, Xiaogang and Huang, Xiaolei and Metaxas, Dimitris N},\r\n  journal={IEEE transactions on pattern analysis and machine intelligence},\r\n  volume={41},\r\n  number={8},\r\n  pages={1947--1962},\r\n  year={2018},\r\n  url_Link = {https://ieeexplore.ieee.org/abstract/document/8411144},\r\n  publisher={IEEE}\r\n}
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\n  \n 2017\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks.\n \n \n \n \n\n\n \n Zhang, H.; Xu, T.; Li, H.; Zhang, S.; Wang, X.; Huang, X.; and Metaxas, D. N\n\n\n \n\n\n\n In Proceedings of the IEEE International Conference on Computer Vision, pages 5907–5915, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"Stackgan: paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{zhang2017stackgan,\r\n  title={Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks},\r\n  author={Zhang, Han and Xu, Tao and Li, Hongsheng and Zhang, Shaoting and Wang, Xiaogang and Huang, Xiaolei and Metaxas, Dimitris N},\r\n  booktitle={Proceedings of the IEEE International Conference on Computer Vision},\r\n  pages={5907--5915},\r\n  url_Paper = {https://arxiv.org/pdf/1710.10916.pdf},\r\n  year={2017}\r\n}
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\n \n\n \n \n \n \n \n \n Integrated local binary pattern texture features for classification of breast tissue imaged by optical coherence microscopy.\n \n \n \n \n\n\n \n Wan, S.; Lee, H.; Huang, X.; Xu, T.; Xu, T.; Zeng, X.; Zhang, Z.; Sheikine, Y.; Connolly, J. L; Fujimoto, J. G; and others\n\n\n \n\n\n\n Medical image analysis, 38: 104–116. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"Integrated paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{wan2017integrated,\r\n  title={Integrated local binary pattern texture features for classification of breast tissue imaged by optical coherence microscopy},\r\n  author={Wan, Sunhua and Lee, Hsiang-Chieh and Huang, Xiaolei and Xu, Ting and Xu, Tao and Zeng, Xianxu and Zhang, Zhan and Sheikine, Yuri and Connolly, James L and Fujimoto, James G and others},\r\n  journal={Medical image analysis},\r\n  volume={38},\r\n  pages={104--116},\r\n  url_Paper = {https://faculty.ist.psu.edu/suh972/Wan_MedIA_2017.pdf},\r\n  year={2017},\r\n  publisher={Elsevier}\r\n}
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\n \n\n \n \n \n \n \n \n Multi-feature based benchmark for cervical dysplasia classification evaluation.\n \n \n \n \n\n\n \n Xu, T.; Zhang, H.; Xin, C.; Kim, E.; Long, L R.; Xue, Z.; Antani, S.; and Huang, X.\n\n\n \n\n\n\n Pattern recognition, 63: 468–475. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"Multi-feature link\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{xu2017multi,\r\n  title={Multi-feature based benchmark for cervical dysplasia classification evaluation},\r\n  author={Xu, Tao and Zhang, Han and Xin, Cheng and Kim, Edward and Long, L Rodney and Xue, Zhiyun and Antani, Sameer and Huang, Xiaolei},\r\n  journal={Pattern recognition},\r\n  volume={63},\r\n  pages={468--475},\r\n  url_Link = {https://www.sciencedirect.com/science/article/abs/pii/S0031320316302941},\r\n  year={2017},\r\n  publisher={Elsevier}\r\n}
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\n  \n 2016\n \n \n (6)\n \n \n
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\n \n\n \n \n \n \n \n \n Diagnostic system and method for biological tissue analysis.\n \n \n \n \n\n\n \n Huang, X.; Wan, S.; and Zhou, C.\n\n\n \n\n\n\n August 11 2016.\n US Patent App. 15/097,780\n\n\n\n
\n\n\n\n \n \n \"Diagnostic link\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@misc{huang2016diagnostic,\r\n  title={Diagnostic system and method for biological tissue analysis},\r\n  author={Huang, Xiaolei and Wan, Sunhua and Zhou, Chao},\r\n  year={2016},\r\n  month=aug # "~11",\r\n  publisher={Google Patents},\r\n  url_Link = {https://patents.google.com/patent/US20160232425A1/en},\r\n  note={US Patent App. 15/097,780}\r\n}
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\n \n\n \n \n \n \n \n System and method for generating three-dimensional images from two-dimensional bioluminescence images and visualizing tumor shapes and locations.\n \n \n \n\n\n \n Metaxas, D.; Banerjee, D.; and Huang, X.\n\n\n \n\n\n\n December 13 2016.\n US Patent 9,519,964\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@misc{metaxas2016system,\r\n  title={System and method for generating three-dimensional images from two-dimensional bioluminescence images and visualizing tumor shapes and locations},\r\n  author={Metaxas, Dimitris and Banerjee, Debabrata and Huang, Xiaolei},\r\n  year={2016},\r\n  month=dec # "~13",\r\n  publisher={Google Patents},\r\n  note={US Patent 9,519,964}\r\n}
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\n \n\n \n \n \n \n \n Model-based curvilinear network extraction toward quantitative microscopy.\n \n \n \n\n\n \n Xu, T.; Zhou, C.; and Huang, X.\n\n\n \n\n\n\n Biomedical Image Segmentation: Advances and Trends,203–232. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{xu2016model,\r\n  title={Model-based curvilinear network extraction toward quantitative microscopy},\r\n  author={Xu, Ting and Zhou, Chao and Huang, Xiaolei},\r\n  journal={Biomedical Image Segmentation: Advances and Trends},\r\n  pages={203--232},\r\n  year={2016},\r\n  publisher={CRC Press}\r\n}
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\n \n\n \n \n \n \n \n Multimodal deep learning for cervical dysplasia diagnosis.\n \n \n \n\n\n \n Xu, T.; Zhang, H.; Huang, X.; Zhang, S.; and Metaxas, D. N\n\n\n \n\n\n\n In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 115–123, 2016. Springer\n \n\n\n\n
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@inproceedings{xu2016multimodal,\r\n  title={Multimodal deep learning for cervical dysplasia diagnosis},\r\n  author={Xu, Tao and Zhang, Han and Huang, Xiaolei and Zhang, Shaoting and Metaxas, Dimitris N},\r\n  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},\r\n  pages={115--123},\r\n  year={2016},\r\n  organization={Springer}\r\n}
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\n \n\n \n \n \n \n \n Traffic-sign detection and classification in the wild.\n \n \n \n\n\n \n Zhu, Z.; Liang, D.; Zhang, S.; Huang, X.; Li, B.; and Hu, S.\n\n\n \n\n\n\n In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2110–2118, 2016. \n \n\n\n\n
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@inproceedings{zhu2016traffic,\r\n  title={Traffic-sign detection and classification in the wild},\r\n  author={Zhu, Zhe and Liang, Dun and Zhang, Songhai and Huang, Xiaolei and Li, Baoli and Hu, Shimin},\r\n  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},\r\n  pages={2110--2118},\r\n  year={2016}\r\n}
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\n \n\n \n \n \n \n \n Spda-cnn: Unifying semantic part detection and abstraction for fine-grained recognition.\n \n \n \n\n\n \n Zhang, H.; Xu, T.; Elhoseiny, M.; Huang, X.; Zhang, S.; Elgammal, A.; and Metaxas, D.\n\n\n \n\n\n\n In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1143–1152, 2016. \n \n\n\n\n
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@inproceedings{zhang2016spda,\r\n  title={Spda-cnn: Unifying semantic part detection and abstraction for fine-grained recognition},\r\n  author={Zhang, Han and Xu, Tao and Elhoseiny, Mohamed and Huang, Xiaolei and Zhang, Shaoting and Elgammal, Ahmed and Metaxas, Dimitris},\r\n  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},\r\n  pages={1143--1152},\r\n  year={2016}\r\n}
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\n  \n 2015\n \n \n (7)\n \n \n
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\n \n\n \n \n \n \n \n Accelerated dynamic MRI reconstruction with total variation and nuclear norm regularization.\n \n \n \n\n\n \n Yao, J.; Xu, Z.; Huang, X.; and Huang, J.\n\n\n \n\n\n\n In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 635–642, 2015. Springer\n \n\n\n\n
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@inproceedings{yao2015accelerated,\r\n  title={Accelerated dynamic MRI reconstruction with total variation and nuclear norm regularization},\r\n  author={Yao, Jiawen and Xu, Zheng and Huang, Xiaolei and Huang, Junzhou},\r\n  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},\r\n  pages={635--642},\r\n  year={2015},\r\n  organization={Springer}\r\n}
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\n \n\n \n \n \n \n \n A new image data set and benchmark for cervical dysplasia classification evaluation.\n \n \n \n\n\n \n Xu, T.; Xin, C.; Long, L R.; Antani, S.; Xue, Z.; Kim, E.; and Huang, X.\n\n\n \n\n\n\n In International Workshop on Machine Learning in Medical Imaging, pages 26–35, 2015. Springer\n \n\n\n\n
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@inproceedings{xu2015new,\r\n  title={A new image data set and benchmark for cervical dysplasia classification evaluation},\r\n  author={Xu, Tao and Xin, Cheng and Long, L Rodney and Antani, Sameer and Xue, Zhiyun and Kim, Edward and Huang, Xiaolei},\r\n  booktitle={International Workshop on Machine Learning in Medical Imaging},\r\n  pages={26--35},\r\n  year={2015},\r\n  organization={Springer}\r\n}
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\n \n\n \n \n \n \n \n Spoke-LBP and ring-LBP: New texture features for tissue classification.\n \n \n \n\n\n \n Wan, S.; Huang, X.; Lee, H.; Fujimoto, J. G; and Zhou, C.\n\n\n \n\n\n\n In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pages 195–199, 2015. IEEE\n \n\n\n\n
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@inproceedings{wan2015spoke,\r\n  title={Spoke-LBP and ring-LBP: New texture features for tissue classification},\r\n  author={Wan, Sunhua and Huang, Xiaolei and Lee, Hsiang-Chieh and Fujimoto, James G and Zhou, Chao},\r\n  booktitle={2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)},\r\n  pages={195--199},\r\n  year={2015},\r\n  organization={IEEE}\r\n}
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\n \n\n \n \n \n \n \n Unbalanced graph-based transduction on superpixels for automatic cervigram image segmentation.\n \n \n \n\n\n \n Huang, S.; Gao, M.; Yang, D.; Huang, X.; Elgammal, A.; and Zhang, X.\n\n\n \n\n\n\n In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pages 1556–1559, 2015. IEEE\n \n\n\n\n
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@inproceedings{huang2015unbalanced,\r\n  title={Unbalanced graph-based transduction on superpixels for automatic cervigram image segmentation},\r\n  author={Huang, Sheng and Gao, Mingchen and Yang, Dan and Huang, Xiaolei and Elgammal, Ahmed and Zhang, Xiaohong},\r\n  booktitle={2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)},\r\n  pages={1556--1559},\r\n  year={2015},\r\n  organization={IEEE}\r\n}
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\n \n\n \n \n \n \n \n Adjustable adaboost classifier and pyramid features for image-based cervical cancer diagnosis.\n \n \n \n\n\n \n Xu, T.; Kim, E.; and Huang, X.\n\n\n \n\n\n\n In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pages 281–285, 2015. IEEE\n \n\n\n\n
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@inproceedings{xu2015adjustable,\r\n  title={Adjustable adaboost classifier and pyramid features for image-based cervical cancer diagnosis},\r\n  author={Xu, Tao and Kim, Edward and Huang, Xiaolei},\r\n  booktitle={2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)},\r\n  pages={281--285},\r\n  year={2015},\r\n  organization={IEEE}\r\n}
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\n \n\n \n \n \n \n \n Multi-test cervical cancer diagnosis with missing data estimation.\n \n \n \n\n\n \n Xu, T.; Huang, X.; Kim, E.; Long, L R.; and Antani, S.\n\n\n \n\n\n\n In Medical Imaging 2015: Computer-Aided Diagnosis, volume 9414, pages 94140X, 2015. International Society for Optics and Photonics\n \n\n\n\n
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@inproceedings{xu2015multi,\r\n  title={Multi-test cervical cancer diagnosis with missing data estimation},\r\n  author={Xu, Tao and Huang, Xiaolei and Kim, Edward and Long, L Rodney and Antani, Sameer},\r\n  booktitle={Medical Imaging 2015: Computer-Aided Diagnosis},\r\n  volume={9414},\r\n  pages={94140X},\r\n  year={2015},\r\n  organization={International Society for Optics and Photonics}\r\n}
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\n \n\n \n \n \n \n \n SOAX: a software for quantification of 3D biopolymer networks.\n \n \n \n\n\n \n Xu, T.; Vavylonis, D.; Tsai, F.; Koenderink, G. H; Nie, W.; Yusuf, E.; Lee, I.; Wu, J.; and Huang, X.\n\n\n \n\n\n\n Scientific reports, 5: 9081. 2015.\n \n\n\n\n
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@article{xu2015soax,\r\n  title={SOAX: a software for quantification of 3D biopolymer networks},\r\n  author={Xu, Ting and Vavylonis, Dimitrios and Tsai, Feng-Ching and Koenderink, Gijsje H and Nie, Wei and Yusuf, Eddy and Lee, I-Ju and Wu, Jian-Qiu and Huang, Xiaolei},\r\n  journal={Scientific reports},\r\n  volume={5},\r\n  pages={9081},\r\n  year={2015},\r\n  publisher={Nature Publishing Group}\r\n}
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\n  \n 2014\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Global contrast based salient region detection.\n \n \n \n\n\n \n Cheng, M.; Mitra, N. J; Huang, X.; Torr, P. H.; and Hu, S.\n\n\n \n\n\n\n IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3): 569–582. 2014.\n \n\n\n\n
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@article{cheng2014global,\r\n  title={Global contrast based salient region detection},\r\n  author={Cheng, Ming-Ming and Mitra, Niloy J and Huang, Xiaolei and Torr, Philip HS and Hu, Shi-Min},\r\n  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},\r\n  volume={37},\r\n  number={3},\r\n  pages={569--582},\r\n  year={2014},\r\n  publisher={IEEE}\r\n}\r\n
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