We are a group working in medical artificial intelligence, exploring the new technologies and applications of artificial intelligence in healthcare. The following themes outline our current research interest:
Medical image analysis
Medical natural language processing
Bioinformatics
In the end, we are trying to integrate all modalities ( including medical images, patient demographics, clinical notes, and genes ) to develop a large multi-modal model and explore its new applications in healthcare.
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2024Founded
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3Postdoctoral Researcher
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3Ph.D Students
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10Visiting Students
Yefeng Zheng, PH.D.
School of Engineering
Medical Artificial Intelligence Laboratory
Yefeng Zheng was born in the city of Jiangshan, Zhejiang Province, in 1975. He received B.E. and M.E. degrees from the Department of Electronic Engineering, Tsinghua University, Beijing, China in 1998 and 2001, respectively, and a Ph.D. degree from the Department of Electrical and Computer Engineering, University of Maryland, College Park, USA in 2005. From 2006 to 2017, he worked at Siemens Corporate Research in Princeton, New Jersey, USA on medical image analysis. From 2018 to 2024, he was a Distinguished Scientist and Director of Tencent Jarvis Lab, leading the company's initiative on medical artificial intelligence. He joined Westlake University in July 2024 as a tenured full professor. He has invented 90+ US patents and published 300+ papers, which have been cited more than 26,000 times with an h-index of 81. He is a Fellow of Institute of Electrical and Electronics Engineers (IEEE), a Fellow of American Institute for Medical and Biological Engineering (AIMBE), and an Associate Editor of IEEE Transactions on Medical Imaging. He was a Program Co-Chair of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2021, served as an area chair for various top conferences in artificial intelligence (e.g., NeurIPS, AAAI, IJCAI, and MICCAI).
MICCAI 2025
A Prior-Driven Lightweight Network for Endoscopic Exposure Correction
Zhijian Wu, Hong Wang, Yuxuan Shi, Dingjiang Huang, and Yefeng Zheng
MICCAI 2025
A Multimodal Contrastive Learning for Detecting Aortic Dissection on 3D Non-Contrast CT with Anatomy Simplification
Duoer Zhang, Wenbo Xiao, Chen Jiang, Yuxuan Qiu, Zhan Feng, Hong Wang, Yefeng Zheng, and Wentao Zhu
MICCAI 2025
TRACE: Temporally Reliable Anatomically-Conditioned 3D CT Generation with Enhanced Efficiency
Minye Shao, Xingyu Miao, Haoran Duan, Zeyu Wang, Jingkun Chen, Yawen Huang, Xian Wu, Jingjing Deng, Yang Long, and Yefeng Zheng
MICCAI 2025
PD-INR: Prior-Driven Implicit Neural Representations for TOF-PET Reconstruction
Yuxuan Long, Yulin Zhang, Hong Wang, Xiaodong Kuang, Hailiang Huang, Fan Rao, Huafeng Liu, Yefeng Zheng, and Wentao Zhu
MICCAI 2025
RRG-DPO: Direct Preference Optimization for Clinically Accurate Radiology Report Generation
Hong Liu, Dong Wei, Zhe Xu, Xian Wu, Yefeng Zheng, and Liansheng Wang
MICCAI 2025
D-CAM: Learning Generalizable Weakly-Supervised Medical Image Segmentation from Domain-invariant CAM
Jingjun Yi, Qi Bi, Hao Zheng, Haolan Zhan, Wei Ji, Huimin Huang, Yuexiang Li, Shaoxin Li, Xian Wu, Yefeng Zheng, and Feiyue Huang
MICCAI 2025
Conservative-Radical Complementary Learning for Class-incremental Medical Image Analysis with Pre-trained Foundation Models
Xinyao Wu, Zhe Xu, Donghuan Lu, Jinghan Sun, Hong Liu, Sadia Shakil, Jiawei Ma, Yefeng Zheng, Raymond Kai-yu Tong
Lancet Digital Health.
Artificial intelligence-assisted detection of nasopharyngeal carcinoma on endoscopic images: a national, multicentre, model development and validation study
Y. Shi, Z. Li, L. Wang, H. Wang, X. Liu, D. Gu, X. Chen, X. Liu, W. Gong, X. Jiang, W. Li, Y. Lin, K. Liu, D. Luo, T. Peng, X. Peng, M. Tong, H. Zheng, X. Zhou, J. Wu, G. El Fakhri, M. Chang, J. Liao, J. Li, D. Wang, J. Ye, S. Qu, W. Jiang, Q. Liu, X. Sun, Y. Zheng, H. Yu
ACL, 2025
Model Merging for Knowledge Editing
Zichuan Fu, Xian Wu, Guojing Li, Yingying Zhang, Yefeng Zheng, Tianshi Ming, Yejing Wang, Wanyu Wang, Xiangyu Zhao
ACL, 2025
Can We Trust AI Doctors? A Survey of Medical Hallucination in Large Language and Large Vision-Language Models
Zhihong Zhu, Yunyan Zhang, Xianwei Zhuang, Fan Zhang, Zhongwei Wan, Yuyan Chen, Qingqing Long, Yefeng Zheng, Xian Wu
3 papers got accepted by the International Conference on Computer Vision. Congrats to Jiale Zhou, Qi Chen, and Qi Bi.
Our paper “A collaborative large language model for drug analysis,” has now been accepted for publication in Nature Biomedical Engineering. Congrats to all co-authors.
7 papers got accepted by International Conference of Medical Image Computing and Computer Assisted Intervention (MICCAI) 2025. Congrats to all co-authors.
5 papers got accepted by The 63rd Annual Meeting of the Association for Computational Linguistics (ACL) 2025. Congrats to all co-authors.
Paper “Artificial Intelligence Assisted Detection of Nasopharyngeal Carcinoma on Endoscopic Images: A National Multi-Center Evidence Study" has been accepted to Lancet Digital Health.
Paper “NightAdapter: Learning a Frequency Adapter for Generalizable Night-time Scene Segmentation” has been accepted to CVPR. Congratulations to Qi Bi!
Paper “GAD: Domain Generalized Diabetic Retinopathy Grading by Grade-Aware De-stylization” has been accepted to Pattern Recognition. Congratulations to Qi Bi!
Paper “Multi-source Domain Adaptation by Causal-guided Adaptive Multimodal Diffusion Networks" has been accepted to International Journal of Computer Vision.
Paper “Structure Observation Driven Image-Text Contrastive Learning for Computed Tomography Report Generation” has been accepted to International Conference on Information Processing in Medical Imaging, 2025.
Paper "LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation" got accepted by Proc. AAAI Conf. Artificial Intelligence 2025. Congrats to all co-authors.
The Medical Artificial Intelligence Laboratory at Westlake University currently has multiple openings for PhD students, postdocs, and research assistants, and we welcome motivated applicants to join our research opportunities. Candidates with a background in computer vision, natural language processing, machine learning, or other related fields are encouraged to apply.