About Me
I am a master’s student in Biomedical Engineering at the College of Biomedical Engineering and Technology Innovation, Fudan University.
My current research focuses on multimodal medical agents, with an emphasis on integrating medical images and clinical information to support clinical reasoning and diagnostic decision-making. I am particularly interested in interactive clinical inquiry and the evaluation of medical agents in realistic clinical scenarios.
Previously, I worked on brain disease diagnosis and brain network analysis, as well as vision-language models (VLMs) for medical imaging.
I welcome opportunities to collaborate on medical agents, multimodal learning, and AI for healthcare. If you share similar research interests, please feel free to reach out to exchange ideas and explore potential collaborations!
Education
College of Biomedical Engineering and Technology Innovation
Supervisor: Zeju Li
College of Information Science and Technology & Artificial Intelligence
Supervisors: Li Zhang and Daoqiang Zhang
Experience
Medical Artificial Intelligence Laboratory
Supervisor: Ke Yan
News
- Sep. 2026 — Our work on self-supervised pretraining for non-contrast CT was accepted by BIBM 2026.
- Sep. 2026 — Joined the College of Biomedical Engineering and Technology Innovation at Fudan University.
- Dec. 2025 — Our work on graph contrastive learning with topology brain augmentation was accepted by Neural Networks.
- Jun. 2025 — Our work on multi-view graph contrastive learning received early acceptance at MICCAI 2025.
Honors
- Jun. 2026 — Outstanding Graduate, Nanjing Forestry University
- Jun. 2026 — Outstanding Undergraduate Thesis, Nanjing Forestry University
- Dec. 2025, Dec. 2024, Dec. 2023 — First-Class Scholarship, Nanjing Forestry University
Publications
LiFUSE: Frequency-Spatial Unified Self-Supervised Pretraining for Liver Disease Analysis on Non-contrast CT
Hao Zhang*, Yi Huang†, Wei Liu, Yuan Gao, Zeju Li, Han Xu, Yu Shi, Ling Zhang, and Ke Yan
IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2026
@InProceedings{zhang2026lifuse,
author = {Zhang, Hao AND Huang, Yi AND Liu, Wei AND Gao, Yuan AND Li, Zeju AND Xu, Han AND Shi, Yu AND Zhang, Ling AND Yan, Ke},
title = {LiFUSE: Frequency-Spatial Unified Self-Supervised Pretraining for Liver Disease Analysis on Non-contrast CT},
booktitle = {Proceedings of the 2026 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
year = {2026}
}Graph-level Contrastive Learning with Self-aware and Cross-sample Topology Augmentation for Brain Disorder Diagnosis Using rs-fMRI
Hao Zhang*, Xiaoyun Liu, Shuo Huang, Yue Ma, Yonggui Yuan†, Daoqiang Zhang†, and Li Zhang†
Neural Networks, 2026
@article{zhang2026graph,
title={Graph-level Contrastive Learning with Self-aware and Cross-sample Topology Augmentation for Brain Disorder Diagnosis Using rs-fMRI},
author={Zhang, Hao and Liu, Xiaoyun and Huang, Shuo and Ma, Yue and Yuan, Yonggui and Zhang, Daoqiang and Zhang, Li},
journal={Neural Networks},
year={2026},
doi={10.1016/j.neunet.2025.108379}
}Multi-view Graph Contrastive Learning with Dynamic Self-aware and Cross-sample Topology Augmentation for Brain Disorder Diagnosis
Hao Zhang*, Xiaoyun Liu, Shuo Huang, Yonggui Yuan†, Daoqiang Zhang†, and Li Zhang†
Medical Image Computing and Computer Assisted Intervention (MICCAI), 2025 · Early Accept
@InProceedings{zhang2025multi,
author = {Zhang, Hao AND Liu, Xiaoyun AND Huang, Shuo AND Yuan, Yonggui AND Zhang, Daoqiang AND Zhang, Li},
title = {Multi-view Graph Contrastive Learning with Dynamic Self-aware and Cross-sample Topology Augmentation for Brain Disorder Diagnosis},
booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2025},
year = {2025},
publisher = {Springer Nature Switzerland},
volume = {LNCS 15971},
month = {September},
page = {532 -- 542}
}
Competitions
- May 2025 — Mathematical Contest in Modeling (MCM), Finalist (Top 1.25%)
- Dec. 2024 — Global Campus Artificial Intelligence Algorithm Elite Competition, National Second Prize