A Survey on Unsupervised Domain Adaptation for Medical Image Segmentation

PhD Qualifying Examination


Title: "A Survey on Unsupervised Domain Adaptation for Medical Image 
Segmentation"

by

Miss Wen JI


Abstract:

Image segmentation is a fundamental task in medical image analysis as it 
provides the labeled regions of interest for the subsequent diagnosis and 
treatment. In recent years, deep neural networks have greatly improved medical 
image segmentation performance. However, the training of deep neural networks 
is highly dependent on data-driven. For many prediction tasks, especially 
dense-estimate tasks, the labeling process is usually expensive, making it 
difficult to collect large-scale and diverse datasets. Particularly, 
fully-supervised segmentation methods for medical images require precise 
annotations from experts, making them more resource-intensive. In addition, due 
to the domain shift between different modalities, when training a model on one 
domain and testing this model on another domain, the performance of the model 
will decrease significantly. This restricts the model from training on 
large-scale labeled datasets and then transfers the model to new datasets from 
different fields. unsupervised domain adaptation (UDA) is an important research 
field in weakly supervised learning based on the idea of transfer learning, it 
provides an effective solution to reduce the domain shift between labeled 
source data and unlabeled target data.

Traditional UDA methods usually use a metric as the objective function to 
measure and minimize the discrepancy of high-dimensional features between the 
source domain and the target domain. Recently, the UDA methods based on 
generative adversarial learning have made some breakthroughs. Some UDA methods 
use the generation approaches as image generators to learn a mapping between 
the source and the target domains, which also have shown promising results in 
various applications.

In this survey, we performed a comprehensive review of unsupervised domain 
adaptation for medical image segmentation. we will introduce existing UDA 
methods including the abovementioned different categories of methods and the 
related works of UDA. With this survey, we aim to facilitate UDA research and 
further explore the insightful future directions for promoting cross-modality 
medical image segmentation.


Date:                   Monday, 3 June 2024

Time:                   2:00pm - 4:00pm

Venue:                  Room 5510
                        Lifts 25/26

Committee Members:      Prof. Albert Chung (Supervisor)
                        Prof. Pedro Sander (Chairperson)
                        Dr. Tristan Braud
                        Dr. Long Chen