Towards Data Scarcity in Medical Image Analysis

PhD Thesis Proposal Defence


Title: "Towards Data Scarcity in Medical Image Analysis"

by

Miss Shuhan LI


Abstract:

Medical image analysis is crucial for providing precise diagnostic support 
through advanced deep-learning techniques. Despite the rapid growth in 
methodologies and applications within this field, data scarcity remains a 
significant challenge. This scarcity typically manifests in three forms: 
limited annotation, imbalanced classes, and the absence of multi-modality data. 
Addressing these issues involves two primary solutions: optimizing learning 
from limited datasets and augmenting data through generative models.

For the first solution, this thesis introduces a novel few-shot learning model 
for the classification of rare skin diseases, named SCAN. SCAN features a 
dual-branch architecture designed to learn both inter and intra-class 
representations, effectively leveraging as few as one to five images to train 
the model. This approach dramatically enhances classification accuracy, 
particularly in scenarios characterized by imbalanced class distributions.

Addressing the second solution, we first propose an iterative online image 
synthesis framework designed to mitigate class imbalance. Unlike traditional 
methods that generate synthetic samples before model training, our framework 
integrates synthesis with training, dynamically adjusting the quantity of 
generated images based on real-time training performance. Then, we expand the 
scope of generative control by incorporating detailed conditions beyond class 
labels, enabling the production of images with higher fidelity and diversity. 
This enhancement is also expected to significantly improve performance in 
downstream tasks. Lastly, to overcome the challenges posed by the absence of 
multi-modality data, we introduce TransPro, a transformative model that employs 
a 3D Generative Adversarial Network (GAN) coupled with two auxiliary tasks to 
translate between Optical Coherence Tomography (OCT) and Optical Coherence 
Tomography Angiography (OCTA). TransPro not only facilitates modality 
conversion but also enriches contextual information and improves the quality of 
vessel regions in the generated images.

Overall, this thesis contributes to the fields of few-shot learning and 
generative modeling by proposing innovative solutions tailored to overcome the 
data scarcity challenges in medical image analysis.


Date:                   Tuesday, 22 October 2024

Time:                   10:00am - 12:00noon

Venue:                  Room 5501
                        Lifts 25/26

Committee Members:      Prof. Tim Cheng (Supervisor)
                        Dr. Xiaomeng Li (Co-supervisor)
                        Dr. Hao Chen (Chairperson)
                        Dr. Long Chen