Generalizable AI for Computed Tomography: From Volumetric Pretraining to Clinical Validation

PhD Thesis Proposal Defence


Title: "Generalizable AI for Computed Tomography: From Volumetric Pretraining 
to Clinical Validation"

by

Mr. Jiaxin ZHUANG


Abstract:

Computed tomography (CT) is a widely used volumetric imaging modality with 
broad clinical applications, including disease detection, diagnosis, staging, 
treatment planning, and longitudinal assessment. Recent CT foundation models 
have enabled a broader range of downstream CT analysis tasks and achieved 
strong performance across retrospective benchmarks, yet their generalizability 
and clinical utility remain insufficiently established, especially in 
prospective and human-AI settings. Developing generalizable CT AI remains 
challenging because CT volumes are high-dimensional, contain complex 
anatomical and a broad range of abnormal findings. Beyond effective volumetric 
learning, clinically relevant CT AI also requires clinical semantic grounding 
and rigorous evaluation across diverse clinical settings.

This thesis addresses these challenges through four sequential studies. 
Bio2Vol shows that knowledge from pretrained two-dimensional biomedical 
foundation models can be effectively transferred to volumetric medical 
imaging. GL-MAE develops dedicated 3D self-supervised pretraining that jointly 
models global anatomical context and local structural detail, yielding 
improved transfer performance across downstream volumetric tasks. MiM further 
introduces hierarchical pretraining across spatial scales and demonstrates 
additional gains with increasing pretraining data. CARE extends these advances 
to large-scale CT-report vision-language pretraining, demonstrating broad 
transfer across heterogeneous tasks and data settings, followed by external, 
prospective, and human-AI validation.

Together, these studies establish a progression from leveraging existing 
biomedical knowledge, through dedicated and scalable volumetric pretraining, 
to vision-language foundation modeling and clinical validation. The findings 
indicate that generalizable CT AI requires coordinated advances in volumetric 
pretraining, clinical semantic grounding, broad transfer, and rigorous 
clinical validation.


Date:                   Monday, 28 September 2026

Time:                   10:00am - 12:00noon

Venue:                  Room 3494
                        Lift 25/26

Committee Members:      Dr. Hao Chen (Supervisor)
                        Dr. Long Chen (Chairperson)
                        Dr. Sehi L'Yi