Applications of Graph Convolutional Networks on Medical Image Analysis: A Survey

PhD Qualifying Examination


Title: "Applications of Graph Convolutional Networks on Medical Image 
Analysis: A Survey"

by

Mr. Yishuo ZHANG


Abstract:

Deep learning methods have revolutionised many machine learning tasks such 
as computer vision and medical data analysis. Benefiting from stacked 
convolution operations and well-designed architecture, neural networks are 
capable of learning rich representations and show promising performances 
on many complicated tasks. The conventional convolution cannot be applied 
directly on graph data which exist universally. Recently, Graph 
Convolutional Networks (GCNs) extend convolution operation from grid space 
to non-grid space such as an irregular graph. These techniques make deep 
learning methods more flexible and effective when dealing with various 
kinds of data, especially for graph data. In this survey, we give a 
comprehensive introduction about the theorem of graph convolution which is 
the fundamental basis of GCNs. With a focus on GCNs applied in medical 
image tasks, related papers which are published on top conferences in 
medical image analysis as well as computer vision areas are reviewed and 
concluded. We introduce different applications of GCNs to show how to 
capture and utilize complex relationships from graph data. Finally, we 
propose some potential research directions in this promising field.


Date:			Monday, 30 September 2019

Time:                  	3:30pm - 5:30pm

Venue:                  Room 2408
                         Lifts 17/18

Committee Members:	Prof. Albert Chung (Supervisor)
 			Prof. Nevin Zhang (Chairperson)
 			Dr. Qifeng Chen
 			Prof. Long Quan


**** ALL are Welcome ****