I am currently a visiting Ph.D. student at the MIMI Lab, Stanford University, supervised by Prof. Akshay Chaudhari, focusing on self-supervised learning for multimodal medical data analysis. Previously, I was a research intern at the Biomedical Imaging team at Microsoft Research, supervised by Shruthi Bannur. I am pursuing my Ph.D. at the Medical AI Lab, The University of Hong Kong, under the supervision of Prof. Lequan Yu. I received my B.Eng. from the Department of Electronic Engineering, Tsinghua University.

I am happy for collaboration on AI for healthcare research! Please ping me if you are interested.

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Scaling Chest X-ray Foundation Models from Mixed Supervisions for Dense Prediction Fuying Wang, Lequan Yu, TMI 2025, [paper] [code]

From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining Fuying Wang*, Jiacheng Xu*, Lequan Yu, ICML 2025, [paper] [code]

CM$^2$: Cross-Modal Alignment via Variational Copula Modelling Feng Wu*, Tsai Hor Chan*, Fuying Wang, Guosheng Yin, Lequan Yu, ICML 2025, [paper] [code]

CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis Fuying Wang*, Feng Wu*, Yihan Tang, Lequan Yu, ACL 2025 Findings, [paper] [code]

TAD-Graph: Enhancing Whole Slide Image Analysis via Task-Aware Subgraph Disentanglement Fuying Wang, Jiayi Xin, Weiqin Zhao, Yuming Jiang, Maximus Yeung, Liansheng Wang, Lequan Yu, TMI 2025, [paper] [code]

HERGen: Elevating Radiology Report Generation with Longitudinal Data Fuying Wang, Shenghui Du, Lequan Yu, ECCV 2024, [paper] [code]

Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning Fuying Wang, Yuyin Zhou, Shujun Wang, Varut Vardhanabhuti, Lequan Yu, NeurIPS 2022, [paper] [code]