Lin YANG, Ph.D.

Artificial Intelligence and Biomedical Image Analysis Lab




Lin YANG, Ph.D.

Artificial Intelligence and Biomedical Image Analysis Lab




“In the interdisciplinary field of artificial intelligence and life sciences, I will continue to explore and innovate at Westlake University, and grow together with it. I also look forward to your joining, let us work together to make AI better serve human health.”


Lin Yang is a Professor at Westlake University and the Director of the Biomedical Image Analysis Lab. He got his bachelor and master degree from Xi’an  Jiaotong University and his Ph. D degree from Rutgers University.


Lin Yang had over 15 years of research experience in biomedical image analysis, imaging informatics, and machine learning with over 100 peer-reviewed journal and conference proceedings. He has served in multiple conference leadership positions during this period of time. At present, this includes service as the General Chair and Program Committee Member of the 4th, 5th, 6th, 7th, and 8th High Performance Computing Workshop Associated with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), the Microscopic Image Analysis with Application in Biology in conjunction with ACM International Conference on Biomedical Informatics and Computational Biology, the Workshop on Sparsity Techniques in Medical Imaging in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, the International Workshop on Machine Learning in Medical Imaging, the VLDB workshop on Data Management and Analytics for Medicine and Healthcare (associated with VLDB), etc. He is serving in the Technical Committee on Biomedical and Health Informatics of the IEEE Engineering in Medicine & Biology Society (BMES), and Scientific Advisory Board Member of Digital Pathology Association (DPA). He is also the Associate Editor of Journal of Pathology Informatics and BMC Bioinformatics.

Our lab will be engaged in the researches which are across fields of medical image analysis, image informatics, computer-aided diagnosis, data mining, machine learning, computer vision, cloud computing and big data for a long time.

Representative Publications

1. 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.

2. 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.

3. 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.

4. 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 InterventionMICCAI). Springer, pp. 320–329, 2020.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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.

10. 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.

11. 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.

12. 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

13. Zizhao Zhang, Fuyong Xing, Xiaoshuang Shi, Lin Yang, “Revisiting Graph Construction for Fast Image Segmentation”, Pattern Recognition (PR), 2018.

14. 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.

15. 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.

16. 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.

17. 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.

18. 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.

19. 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.

20. 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.

Our lab has multiple positions including post-doctoral fellows, doctoral students and research assistants in computer, biomedical engineering, and related majors. Those who are interested in the research direction of our lab are welcome to join!