A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning. - Zekun Li 010; Wei Zhao 040; Feng Shi 001; Lei Qi 001; Xingzhi Xie; Ying Wei 009; Zhongxiang Ding; Yang Gao 001; Shangjie Wu; Jun Liu 075; Yinghuan Shi; Dinggang Shen (Medical Image Anal., 2021)

IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization. - Zekun Li 010; Lei Qi 001; Yinghuan Shi; Yang Gao 001 (ICCV, 2023)

Open-Domain Semi-Supervised Learning via Glocal Cluster Structure Exploitation. - Zekun Li 010; Lei Qi 001; Yawen Li 001; Yinghuan Shi; Yang Gao 001 (IEEE Trans. Knowl. Data Eng., 2024)

Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation. - Qinghe Ma; Jian Zhang 018; Zekun Li 010; Lei Qi 001; Qian Yu; Yinghuan Shi (CVPR, 2025)

Taste More, Taste Better: Diverse Data and Strong Model Boost Semi-Supervised Crowd Counting. - Maochen Yang; Zekun Li 010; Jian Zhang 018; Lei Qi 001; Yinghuan Shi (CVPR, 2025)

Balancing Multi-Target Semi-Supervised Medical Image Segmentation With Collaborative Generalist and Specialists. - You Wang; Zekun Li 010; Lei Qi 001; Qian Yu; Yinghuan Shi; Yang Gao 001 (IEEE Trans. Medical Imaging, 2025)