Medical Images Super-resolution by Deep Learning: A Survey

PhD Qualifying Examination


Title: "Medical Images Super-resolution by Deep Learning: A Survey"

by

Mr. Siu Chung TSANG


Abstract:

Medical imaging has demonstrated many promising clinical applications. However, 
the potential of medical imaging is constrained by its resolution. The 
resolution of medical images is often limited by scanning time, patient 
movement, or hardware settings. Thus, the super-resolution method is desired to 
enhance the quality of medical images. Recently, deep learning-based 
super-resolution models have shown encouraging results in both natural images 
and medical images. This paper is an effort to provide a detailed survey on the 
current research trends of super-resolution by deep learning. We first 
introduce the problem definitions, the measurement metrics, and the benchmark 
datasets. Next, we present the deep learning paradigms that are employed by 
recent methods. The advantages and challenges of each method are highlighted. 
Moreover, we point out the major barriers for delivering clinical impact and 
propose some future research directions to address these challenges.


Date:			Wednesday, 26 January 2022

Time:                  	3:00pm - 5:00pm

Zoom Meeting:		https://hkust.zoom.us/j/6807545958

Committee Members:	Prof. Albert Chung (Supervisor)
 			Prof. Chi-Keung Tang (Supervisor)
 			Prof. Raymond Wong (Chairperson)
 			Dr. Xiaojuan Ma
 			Dr. Dan Xu


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