DOMAIN-GENERALIZED RECOGNITION AND GENERATION FOR MEDICAL IMAGE ANALYSIS

PhD Thesis Proposal Defence


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

by

Mr. Haoxuan CHE


Abstract:

Medical image analysis plays a crucial role in modern healthcare, enabling 
automated interpretation and assistance in clinical decision-making.  While 
deep learning has demonstrated remarkable potential and achieved 
state-of-the-art performance in various medical tasks in this field, 
significant challenges emerge when deploying these models in real-world 
clinical settings, particularly in the context of domain insufficiency and 
domain isolation. Domain insufficiency arises from the limited availability 
and diversity of training data within individual medical centers, which 
contributes to poor generalization and significant performance degradation 
when models are deployed in new clinical environments. Additionally, while 
valuable medical data exists across institutions, it remains isolated in 
separate silos owing to strict privacy regulations, and proprietary concerns, 
hindering the development of robust, generalizable models. To address these 
challenges, our research contributions are organized into two complementary 
directions.  First, we develop a series of frameworks that learn under domain 
insufficiency by enhancing model robustness and generalization capability 
when training with limited source domains. Our approaches focus on learning 
domain-invariant representations, adapting to quality variations, and 
handling data distribution shifts. Second, we propose privacy-preserving 
collaborative learning solutions that overcome domain isolation by enabling 
effective knowledge sharing across institutions while maintaining data 
privacy.  These approaches span from generative model-based to large 
model-based knowledge sharing, creating a bridge between isolated medical 
data silos. Through extensive experiments across various medical recognition 
and generation tasks, we demonstrate that our proposed approaches 
significantly improve cross-domain performance while maintaining practical 
efficiency and privacy requirements.  These contributions advance both the 
technical framework and practical applications of medical image analysis, 
working towards more generalizable and deployable AI systems in real-world 
clinical settings.


Date:                   Friday, 7 March 2025

Time:                   3:00pm - 5:00pm

Venue:                  Room 4472
                        Lifts 25/26

Committee Members:      Dr. Hao Chen (Supervisor)
                        Dr. Dan Xu (Chairperson)
                        Dr. Long Chen