DOMAIN-GENERALIZED RECOGNITION AND GENERATION FOR MEDICAL IMAGE ANALYSIS

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "DOMAIN-GENERALIZED RECOGNITION AND GENERATION FOR MEDICAL IMAGE 
ANALYSIS"

By

Mr. Haoxuan CHE


Abstract:

Medical image analysis plays a pivotal role in modern healthcare by 
facilitating automated interpretation and enhancing clinical 
decision-making. While deep learning has achieved state-of-the-art 
performance in various medical imaging tasks, its deployment in real-world 
clinical settings remains challenging, particularly due to issues related to 
domain data limitation and domain data isolation. Domain data limitation 
stems from the limited availability and diversity of training data within 
individual medical institutions, leading to poor model generalization and 
performance degradation when applied to unseen clinical environments. 
Simultaneously, although vast amounts of valuable medical data are 
distributed across institutions, strict privacy regulations and proprietary 
constraints result in domain data isolation, preventing effective cross- 
institutional knowledge sharing and limiting the development of robust and 
generalizable models.

To address these challenges, this thesis contributes in two key directions. 
First, it introduces a series of frameworks designed to enhance model 
robustness and generalization in scenarios characterized by domain data 
limitation. These approaches focus on learning domain-invariant 
representations, mitigating quality variations, and adapting to 
distributional shifts, thereby improving performance across diverse clinical 
settings. Second, it proposes privacy-preserving collaborative learning 
strategies to mitigate domain data isolation by enabling knowledge transfer 
across institutions without compromising data privacy. These strategies 
range from generative model- based solutions to large-scale model-based 
knowledge sharing, facilitating collaboration while maintaining compliance 
with privacy constraints. Through extensive experimental validation across 
various medical recognition and generation tasks, this thesis demonstrates 
that the proposed methodologies significantly enhance cross-domain 
performance while maintaining computational efficiency and privacy 
safeguards. The findings contribute to both the theoretical advancement and 
practical deployment of AI- driven medical image analysis, moving towards 
the development of more generalizable and clinically deployable systems.


Date:                   Monday, 12 May 2025

Time:                   4:30pm - 6:30pm

Venue:                  Room 5501
                        Lifts 25/26

Chairman:               Prof. Dan LI (CBE)

Committee Members:      Dr. Hao CHEN (Supervisor)
                        Dr. Long CHEN
                        Dr. Dan XU
                        Dr. Jiguang WANG (LIFS)
                        Prof. Jing QIN (PolyU)