Deep Learning for Computational Cytopathology: From Cellular Analysis to Whole-Slide Screening

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


PhD Thesis Defence


Title: "Deep Learning for Computational Cytopathology: From Cellular
Analysis to Whole-Slide Screening"

By

Mr. Hao JIANG


Abstract:

Computational cytopathology enables precancerous screening through 
cytomorphological assessment of cellular specimens, yet clinical translation 
faces fundamental challenges from morphological ambiguity, spatial crowding, 
and domain shifts across multi-center cohorts. This thesis establishes a 
systematic framework progressing from cellular analysis to whole-slide 
screening. It first addresses categorical ambiguity and imbalance via HERO, a 
holistic and historical instance comparison framework that enforces RoI-level 
and class-level contrastive consistency with a confidence-gated memory bank, 
enabling robust instance characterization under subtle inter-class 
discrepancies and long-tailed distributions. For segmentation under spatial 
overlapping, DoNet introduces a decomposition-recombination strategy with a 
dual-path region segmentation module and semantic consistency-guided 
refinement to disentangle translucent overlapping cell clusters, further 
reinforced by a mask-guided region proposal module that imposes the biological 
prior of nuclei-cytoplasm containment. To generalize structure-aware 
refinement beyond cytology, GAInS develops anomaly-guided supervision through 
gradient-field modeling and adaptive local refinement, precisely delineating 
crossing, touching, and overlapping instances across diverse pathological 
scenarios. At the patient level, these insights inform Smart-CCS, a 
generalizable whole-slide screening architecture integrating self-supervised 
pretraining on 127,471 whole-slide images from 48 centers, multi-instance 
aggregation, and test-time domain adaptation with prototype alignment, 
translating cellular evidence into actionable decisions across heterogeneous 
clinical settings with prospective and histological validation. Together, 
these methods establish an evolutionary path from cellular morphology to 
holistic diagnostic reasoning, demonstrating that morphology-aware learning, 
structure-aware segmentation, and generalizable aggregation can be integrated 
to advance AI-assisted cytopathology for precancerous screening and patient 
triage.


Date:                   Tuesday, 25 August 2026

Time:                   3:00pm - 5:00pm

Venue:                  Room 5501
                        Lifts 25/26

Chairman:               Prof. Jianan QU (ECE)

Committee Members:      Dr. Hao CHEN (Supervisor)
                        Prof. Pedro SANDER
                        Dr. Dan XU
                        Dr. Terence Tsz Wai WONG (CBE)
                        Prof. Harry Jing QIN (PolyU)