The Tissue Image Analytics (TIA) Centre hosts a seminar series for those interested in computational pathology. The series invites researchers and leaders in computational pathology and related fields to present their work and foster thought-provoking discussions.

This seminar features Dr Junlin Hou from The Hong Kong University of Science and Technology, Hong Kong.

Abstract

Computational pathology is rapidly moving from task-specific image analysis toward foundation models that enable scalable, generalizable disease diagnosis and clinical decision support. In this talk, I will first present our recent work, PathSegmentor, a foundation model that enables flexible, text-prompted segmentation across diverse pathological entities. Beyond segmentation, I will also provide an overview of our broader efforts in pathology foundation models, including image enhancement, whole-slide representation learning, benchmarking, and agentic AI systems. These works aim to advance computational pathology toward more precise, interpretable, and clinically useful AI for precision oncology.

Bio

Dr. Junlin Hou is a Post-doctoral Fellow in the Department of Computer Science and Engineering at The Hong Kong University of Science and Technology (HKUST). She obtained her Ph.D. from the School of Computer Science at Fudan University in 2023. She has published 30+ papers in top-tiered journals and conferences, including Nature Computational Science, Nature Biomedical Engineering, IEEE TMI, MICCAI, CVPR, etc. She has served as a guest editor for CMIG and J. Imaging, and as a reviewer for 15+ journals and conferences. She also led the team winning 10+ medical grand challenges. Her current research interest is in trustworthy medical AI and computational pathology.