Deep Learning for Computational Cytopathology

PhD Thesis Proposal Defence


Title: "Deep Learning for Computational Cytopathology"

by

Mr. Hao JIANG


Abstract:

Computational cytopathology holds promise for precancerous screening through 
morphometric assessment of cellular specimens, yet clinical translation is 
constrained by morphological ambiguity, spatial crowding, and domain shifts 
across multi-center cohorts. This proposal establishes a systematic framework 
progressing from instance-level analysis to whole-slide screening. The work 
surveys prevailing paradigms for cytological perception and weakly-supervised 
classification, then addresses categorical ambiguity via contrastive 
feature-space consistency for robust instance characterization. For 
segmentation under spatial occlusion, a decomposition-recombination strategy 
disentangles crowded clusters, extended by structure-aware refinement with 
anomaly-guided supervision for precise morphological delineation. At the 
patient level, these insights inform a generalizable screening architecture 
integrating self-supervised pretraining, multi-instance aggregation, and 
test-time domain adaptation-translating cellular evidence into actionable 
decisions across heterogeneous settings. Collectively, this work charts an 
evolutionary path from cellular morphometrics to holistic diagnostic 
reasoning, advancing AI-assisted cytopathology for precancerous screening and 
patient triage.


Date:                   Monday, 6 July 2026

Time:                   3:00pm - 5:00pm

Venue:                  Room 3494
                        Lifts 25/26

Committee Members:      Dr. Hao Chen (Supervisor)
                        Prof. Pedro Sander (Chairperson)
                        Dr. Terence Tsz Wai Wong (CBE)