Enhancing Representation Capability of Deep Learning Models for Medical Image Analysis under Limited Training Data

Prof. QIN Jing
Centre for Smart Health
School of Nursing
The Hong Kong Polytechnic University

Deep learning has achieved remarkable success in various medical image analysis tasks. No matter the past, present, or the foreseeable future, one of the main obstacles that prohibits deep learning models from being successfully developed and deployed in clinical settings is the scarcity of training data. In this talk, we shall review, as well as rethink, our long experience in investigating how to enhance representation capability of deep learning models to achieve satisfactory performance under limited training data. Based on our experience, we attempt to identify and sort out the evolution trajectory of applying deep leaning to medical image analysis, somehow reflecting the development path of deep learning itself beyond the context of our specific applications. The models we developed, at least in our experience, are both effects and causes: effects of the clinical challenges we faced and the technical frontiers at that time; causes, if they are really useful and inspiring, of following more advanced models that are capable of addressing their limitations. To the end, by rethinking such an evolution, we can identify some future directions that deserve to be further studied.

QIN, Jing (Harry) is currently an associate professor in Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University. His research focuses on creatively leveraging advanced virtual/augmented reality (VR/AR) and artificial intelligence (AI) techniques in healthcare and medicine applications and his achievements in relevant areas has been well recognized by the academic community. He won the Hong Kong Medical and Health Device Industries Association Student Research Award for his PhD study on VR-based simulation systems for surgical training and planning. He won 5 best paper awards for his research on AI-driven medical image analysis and computer-assisted surgery. He served as a local organization chair for MICCAI 2019, program committee members for AAAI, IJCAI, MICCAI, etc., speakers for many conferences, seminars, and forums, and referees for many prestigious journals in relevant fields.

Enquiries: Mr Jeff Liu at Tel. 3943 0624


Dec 01, 2022


2:30 pm - 3:30 pm


Room 121, 1/F, Ho Sin-Hang Engineering Building, CUHK

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