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ZHANG Li

  • Assistant Principal Investigator
  • Academy for Advanced Interdisciplinary Studies, Peking University
    National Biomedical Imaging Center
  • 个人简介
    Based on the Data Science Research Center of Peking University, the Biomedical Image Analysis Laboratory was established on March 7, 2017. The laboratory aims to provide a research platform for researchers in the fields of mathematics, physics, engineering, computer, information science, biology, medicine and industry to promote the research and exchange of biomedical imaging technology, biomedical image analysis and auxiliary diagnosis supported by data science. The laboratory has obtained a number of cutting-edge scientific research achievements in the fields of pain points such as small sample learning and knowledge transfer for biomedical images, and published papers in academic journal conferences including MedIA, Nat. Commun, MICCAI, AAAI, IJCAI, etc.
  • 个人履历
    2016/08-present, Beijing Institute of Big Data Research, Academy for Advanced Interdisciplinary Studies, Peking University, Assistant Principal Investigator
    2015/12-2016/07, Postdoctoral Researcher, Iowa Institute of Biomedical Engineering, USA
    2010/08-2015/12, University of Iowa, School of Electrical and Computer Engineering, PhD
    2006/09-2010/06, School of Physics, Wuhan University, Bachelor Degree
  • 代表性论文及论著
    1. Hexin Dong, Zifan Chen, Mingze Yuan, Yutong Xie, Jie Zhao, Fei Yu, Bin Dong, Li Zhang*. Region-Aware Metric Learning for Open World Semantic Segmentation via Meta-Channel Aggregation[J]. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. Main Track. Pages 863-869, 2022.
    2. Fei Yu, Mo Zhang, Hexin Dong, Sheng Hu, Bin Dong, Li Zhang*. Dast: Unsupervised domain adaptation in semantic segmentation based on discriminator attention and self-training[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2021, 35(12): 10754-10762.
    3. Fei Yu, Hexin Dong, Mo Zhang, Jie Zhao, Bin Dong, Quanzheng Li, Li Zhang*. AF-SEG: An Annotation-Free Approach for Image Segmentation by Self-Supervision and Generative Adversarial Network. IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020.
    4. Mo Zhang, Jie Zhao, Xiang Li, Li Zhang*, Quanzheng Li. ASCNet: Adaptive-Scale Convolutional Neural Networks for Multi-Scale Feature Learning. IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020.
    5. Fei Yu, Jie Zhao, Yanjun Gong, Zhi Wang, Yuxi Li, Fan Yang, Bin Dong, Quanzheng Li, Li Zhang*. “Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images”. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2019.