Automated cancer detection on histopathology images using deep convolutional neural networks

PhD Qualifying Examination


Title: "Automated cancer detection on histopathology images using deep 
convolutional neural networks"

by

Mr. Yongxiang HUANG


Abstract:

Histopathology imaging allow pathologists to access the microscopic 
structure and elements of biopsy specimens, which is an essential 
diagnostic method to finalize the potential diseases such as cancer. 
Unlike natural images, pathology images require highly skilled 
pathologists to review, which is fairly time-consuming and error-prone. 
With the increasing ability to rapidly digitalize whole slide images with 
slide scanners, research communities, medical intuitions and companies 
raise interests in developing computer-assisted diagnostic algorithms for 
automatic detection of disease extent from pathology images. Deep learning 
of digitalized pathology slides offers the potential to reduce 
misdiagnosis and improve the speed of screening.

This survey aims to give a comprehensive overview of automated cancer 
detection on histopathology images using deep convolution neural networks. 
We first introduce the workflow of digital histopathology analysis, in 
which state-of-the-art methods for histology images classification in 
glimpse-level and side-level are reviewed. Then we review methods for 
cancer metastasis detection and localization on giga-pixel histopathology 
images of lymph node section. Finally, we discuss the potential directions 
for further research on detecting lesions on histopathology images.


Date:			Wednesday, 6 March 2019

Time:                  	10:00am - 12:00noon

Venue:                  Room 3494
                         Lifts 25/26

Committee Members:	Prof. Albert Chung (Supervisor)
 			Prof. Chiew-Lan Tai (Chairperson)
 			Prof. Long Quan
 			Dr. Pedro Sander


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