Medical Images Denoising and Super-Resolution by Deep Learning

PhD Thesis Proposal Defence


Title: "Medical Images Denoising and Super-Resolution by Deep Learning"

by

Mr. Siu Chung TSANG


Abstract:

Medical images usually suffer from high noises and limited resolution. In many 
medical image modalities, the noise level and resolution are restricted by 
various reasons such as the integrity of the sample, patient movement, signal 
interference, scanning time, hardware settings, and the list goes on. 
Post-processing applications such as segmentation, diagnosis, and detection 
rely on a high-quality input image. The doctor's decision on treatment planning 
or biologist's research study also depends on the noise level and resolution of 
an image.

Deep learning-based denoising and super-resolution models today have shown 
encouraging results in both natural images and medical images. A major 
limitation in the literature is that these two tasks are addressed separately. 
Recently, studies have shown that the joint denoising and super-resolution 
(JDSR) approach outperforms the sequential application of the denoiser and 
super-resolution model. Despite having promising results, the JDSR approach is 
relatively unexplored due to the absence of a suitable dataset. The training 
process of these methods requires noise-free ground truth or multiple noisy 
captures. However, these extra training datasets are often unavailable in many 
medical image applications. This manuscript proposes a new weakly-supervised 
method, in which different from other approaches, the JDSR model is trained 
with a single noisy-HR capture alone. We further introduce a novel training 
framework to approximate the supervised JDSR approach. We present both 
theoretical explanation and experimental analysis for our method validation. 
Moreover, we give a fully-unsupervised approach for JDSR when HR training data 
is not available.


Date:			Tuesday, 9 August 2022

Time:                  	4:00pm - 6:00pm

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

Committee Members:	Prof. Albert Chung (Supervisor)
 			Prof. Pedro Sander (Supervisor)
 			Dr. Dimitris Papadopoulos (Chairperson)
 			Dr. Dan Xu


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