Deep Learning for Computational Cytopathology: A Survey

PhD Qualifying Examination


Title: "Deep Learning for Computational Cytopathology: A Survey"

by

Mr. Hao JIANG


Abstract:

Cytopathology enables early cancer detection through microscopic evaluation 
of cellular morphology to identify precancerous lesions and malignancies. 
With advancement of artificial intelligence (AI), computational 
cytopathology (Ccyto) increasingly leverages deep learning (DL)-based 
computer-aided techniques to enhance the traditional cytopathology workflow, 
potentially accelerating analysis tenfold and improving sensitivity. This 
survey reviews over 200 literatures on DL-based Ccyto, focusing on advanced 
DL methodologies and commercial applications. Specifically, we outline the 
Ccyto workflow, emphasizing key DL tasks: cytomorphological segmentation for 
delineating cellular structures, cellular identification for categorizing 
cancerous cell types, and whole slide classification for patient-level 
precancerous screening. We introduce DL paradigms, public datasets, and 
evaluation metrics. Finally, we discuss challenges and future directions for 
DL in computational cytopathology.


Date:                   Tuesday, 13 May 2025

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)