个人简介
杨林，1999年毕业于西安交通大学，获工学学士学位；2002年获西安交通大学信息与通讯工程硕士学位；2006年、2009年分别获罗格斯大学电子与计算机工程硕士和博士学位。2009年至2011年，担任罗格斯大学病理学系、放射学系和生物医学工程系的助理教授。2011年至2014年，担任计算机科学系的助理教授，并于2014年获得终身副教授。2020年加入西湖大学工学院，成立人工智能与生物医学影像实验室，从事人工智能、医学影像、机器学习等医工交叉方面的研究工作。
学术成果
杨林博士聚焦生物医学图像分析、图像信息学和机器学习领域，已有超过15年的研究经验。在利用大数据进行计算机辅助诊断和预测、生物医学图像分析、数字病理和人工智能领域做出了重大贡献。他曾在MICCAI等国际大会中获得2015年青年科学家最佳论文奖等奖项，近年来已在Nature Machine Intelligence、Nature Medicine等国际期刊和CVPR、MICCAI等会议论文集上发表科研论文一百余篇，引用已达上万次。
杨林博士曾担任第四届至第八届高性能计算研讨会、IEEE医药与生物工程学会（BMES）等多个会议的领导职务，是 Journal of Pathology Informatics和BMC Bioinformatics的副编辑及IEEE、BMES、EMBS、ESPS、MICCAI、IEEE计算机学会的专业会员。
本课题组长期从事医学图像分析、图像信息学、计算机辅助诊断、数据挖掘、机器学习、计算机视觉、云计算和大数据等领域的研究。
代表论文
1. Honglin Li, Chenglu Zhu, Yunlong Zhang, Yuxuan Sun, Zhongyi Shui, Wenwei Kuang, Sunyi Zheng, Lin Yang,“Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classification“, CVPR, 2023.
2. Li Yuan, Lin Yang, Shichuan Zhang, Zhiyuan Xu, Jiangjiang Qin, Yunfu Shi, Pengcheng Yu, Yi Wang, Zhehan Bao, Yuhang Xia, Jiancheng Sun, Weiyang He, Tianhui Chen, Xiaolei Chen, Can Hu, Yunlong Zhang, Changwu Dong, Ping Zhao, Yanan Wang, Nan,Jiang, Bin Lv, Yingwei Xue, Baoping Jiao, Hongyu Gao, Kequn Chai, Jun Li, Hao Wang, Xibo Wang, Xiaoqing Guan, Xu Liu, Gang Zhao, Zhichao Zheng, Jie Yan, Haiyue Yu, Luchuan Chen, Zaisheng Ye, Huaqiang You, Yu Bao, Xi Cheng, Peizheng Zhao, Liang Wang, Wenting Zeng, Yanfei Tian, Ming Chen You You, Guihong Yuan, Hua Ruan, Xiaole Gao, Jingli Xu, Handong Xu, Lingbin Du, Shengjie Zhang, Huanying Fu, Xiangdong Cheng, "Development of a tongue image-based machine learning tool for the diagnosis of gastric cancer: a prospective multicentre clinical cohort study", eClinicalMedicine, Vol.57, 101834, 2023.
3. Jingxiong Li, Sunyi Zheng, Zhongyi Shui, Shichuan Zhang, Linyi Yang, Yuxuan Sun, Yunlong Zhang, Honglin Li, Yuanxin Ye, Peter M.A. van Ooijen, Kang Li, Lin Yang, Masked conditional variational autoencoders for chromosome straightening，IEEE TRANSACTIONS ON MEDICAL IMAGING, 2023.
4. Sunyi Zheng*, Jingxiong Li*, Zhongyi Shui, Chenglu Zhu, Yunlong Zhang, Pingyi Chen, Lin Yang, ”ChrSNet: Chromosome Straightening using Self-attention Guided Networks”, MICCAI, 2022.
5. Zhongyi Shui*, Shichuan Zhang*, Chenglu Zhu, Bingchuan Wang, Pingyi Chen, Sunyi Zheng, Lin Yang, “End-to-End cell recognition by point annotation”, MICCAI, 2022.
6. Yunlong Zhang*, Yuxuan Sun*, Honglin Li, Sunyi Zheng, Chenglu Zhu, Lin Yang, ”Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology”, MICCAI, 2022.
7. Jiatong Cai, Chenglu Zhu, Can Cui, Honglin Li, Tong Wu, Shichuan Zhang, Lin Yang, “Generalizing Nucleus Recognition Model in Multi-source Ki67 Immunohistochemistry Stained Images via Domain-specific Pruning”, MICCAI, 2021.
8. X. Shi, F. Xing, Z. Zhang, M. Sapkota, Z. Guo, and L.Yang, “A scalable optimization mechanism for pairwise based discrete hashing”, IEEE Transactions on Image Processing, vol. 30, pp. 1130–1142, 2020.
9. Xiaoshuang Shi,Zhenhua Guo,Fuyong Xing,Yun Liang, Lin Yang, “Anchor-based self-ensembling for semi-supervised deep pairwise hashing”,International Journal of Computer Vision, pp. 1-18, 2020.
10. H. Li, X. Han, Y. Kang, X. Shi, M. Yan, Z. Tong, Q. Bu, L. Cui, J. Feng, and L. Yang, “A novel loss calibration strategy for object detection networks training on sparsely annotated pathological datasets”, in International Conference on Medical Image Computing and Computer-Assisted Intervention（MICCAI）. Springer, pp. 320–329, 2020.
11. X. Shi, F. Xing, Y. Xie, Z. Zhang, L. Cui, and L. Yang, “Loss-based attention for deep multiple instance learning”, AAAI Conference on Artificial Intelligence, Vol.34, No.04, pp. 5742–5749, 2020.
12. P. Chen, J. Cai, and L.Yang,“Chromosome segmentation via data simulation and shape learning”, in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).IEEE, pp. 1637–1640, 2020.
13. X Shi, H Su, F Xing, Y Liang, G Qu, L Yang,“Graph temporal ensembling based semi-supervised convolutional neural network with noisy labels for histopathology image analysis”, Medical Image Analysis, 2020.
14. Zizhao Zhang, Pingjun Chen, Mason McGough, Fuyong Xing, Chunbao Wang, Marilyn Bui, Yuanpu Xie, Manish Sapkota, Lei Cui, Jasreman Dhillon, Nazeel Ahmad, Farah K. Khalil, Shohreh I. Dickinson, Xiaoshuang Shi, Fujun Liu, Hai Su, Jinzheng Cai, Lin Yang, “Pathology-level interpretable whole-slide cancer diagnosis with deep learning”, Nature Machine Intelligence, Vol.1, pp.236-245, 2019.
15. Jinzheng Cai, Zizhao Zhang, Lei Cui, Yefeng Zheng, Lin Yang, “Towards cross-modal organ translation and segmentation: A cycle- and shape-consistent generative adversarial network”, Medical Image Analysis, Vol.52, pp.174-184, 2019.
16. Hai Su, Lin Yang, “Local and global consistency regularized mean teacher for semi-supervised nuclei classification”, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019.
17. Zizhao Zhang, Pingjun Chen, Xiaoshuang Shi, Lin Yang, “Text-guided neural network training to recognize images in nature scene and medicine”, IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI), 2019.
18. Manish Sapkota, Xiaoshuang Shi, Fuyong Xing, Lin Yang, “Deep Convolutional Hashing for Low Dimensional Binary Embedding of Histopathological Images”, IEEE Journal of Biomedical and Health Informatics,Vol.23,No.2, pp. 805-816, 2019
19. Zizhao Zhang, Fuyong Xing, Xiaoshuang Shi, Lin Yang, “Revisiting Graph Construction for Fast Image Segmentation”, Pattern Recognition (PR), 2018.
20. Zizhao Zhang*, Yuanpu Xie*, Lin Yang, “Photographic Text-to-Image Synthesis with a Hierarchically-nested Adversarial Network”, International Conference on Computer Vision and Pattern Recognition (CVPR), pp.6199-6208, 2018.
21. Zizhao Zhang, Yuanpu, Xie, Fuyong Xing, Mason Mcgough, Lin Yang, “MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
22. Yuanpu Xie, Zizhao Zhang, Manish Sapkota, Lin Yang, "Spatial Clockwork Recurrent Neural Network for Muscle Perimysium Segmentation", in the 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2016.
23. Fuyong Xing, Lin Yang, “An automatic learning-based framework for robust nucleus segmentation”, IEEE Transaction on Medical Imaging, Vol.35, No.2, pp. 550-566, 2016.
24. Christopher S. Fry, Jonah D. Lee, Jyothi Mula, Tyler J. Kirby, Janna R. Jackson, Fujun Liu, Lin Yang, Esther E. Dupont-Versteegden, John J. McCarthy, Charlotte A. Peterson, "Inducible depletion of satellite cells in adult, sedentary mice impairs muscle regenerative capacity without affecting sarcopenia", Nature Medicine, Vol. 21, pp. 76-80, 2015.
25. Lin Yang, Xin Qi, Fuyong Xing, Tahsin Kurc, Joel Saltz, David J. Foran, “Parallel Content Based Sub-image Retrieval Using Hierarchical Searching”, Bioinformatics, Vol.30, No.7, pp. 996-1002, 2014.
26. Nicolas Wein, Adeline Vulin, Maria Sofia Falzarano, Christina Al-Khalili Szigyarto, Baijayanta Maiti, Andrew Findlay, Kristin H. Heller, Mathias Uhlen, Baskar Bakthavachalu, Sonia Messina, Giuseppe Vita, Chiara Passarelli, Francesca Gualandi, Steve D. Wilton, Louise Rodino-Klapec, Lin Yang, Diane M. Dunn, Daniel Schoenberg, Robert B. Weiss, Michael T. Howard, Alessandra Ferlini, Kevin M. Flanigan, "Translation from a DMD exon 5 IRES results in a functional dystrophin isoform that attenuates dystrophinopathy in humans and mice", Nature Medicine, Vol.20, No.9, pp. 992-1000, 2014.
联系方式
课题组现提供计算机、生物医学工程及相关专业的博士后、博士生和科研助理等多个职位。欢迎对本课题组研究方向感兴趣的人才加入！