Cheng Ouyang
Dr. Cheng Ouyang is a Departmental Lecturer at the Institute of Biomedical Engineering, Department of Engineering Science. His research centres on data-efficient, robust, and user-friendly machine learning approaches for medical image/signal computing. His research topics include but are not limited to domain generalization, few-/zero-shot learning, uncertainty modeling, and multimodal machine learning for the interpretation and analysis of medical data, primarily images such as ultrasound and MRI. Prior to joining Oxford, he was a postdoctoral researcher on cardiac imaging at the Institute of Clinical Sciences, Imperial College London. He obtained his PhD from the Department of Computing at Imperial College London.
Recent publications
MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning
Conference paper
Pan J. et al, (2026), Lecture Notes in Computer Science, 15966 LNCS, 337 - 347
Stability and Generalizability in SDE Diffusion Models with Measure-Preserving Dynamics
Conference paper
Zhang W. et al, (2024), Advances in Neural Information Processing Systems, 37
Universal Topology Refinement for Medical Image Segmentation with Polynomial Feature Synthesis
Conference paper
Li L. et al, (2024), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 15009 LNCS, 670 - 680
G2D: From Global to Dense Radiography Representation Learning via Vision-Language Pre-training
Conference paper
Liu C. et al, (2024), Advances in Neural Information Processing Systems, 37