Towards Data Scarcity in Medical Image Analysis

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Towards Data Scarcity in Medical Image Analysis"

By

Miss Shuhan LI


Abstract:

Deep learning has revolutionized computer vision, with significant 
applications in classification, segmentation, image translation, and 
super-resolution. These techniques are particularly vital in medical imaging, 
where they enhance disease diagnosis and treatment. However, achieving 
accurate and robust predictions in this field often requires large training 
datasets and precise annotations, which are frequently scarce. This 
dissertation addresses three critical challenges: (1) Class Imbalance, as 
data for common diseases is readily available while rare diseases are 
underrepresented; (2) Lack of Annotations, since pixel-level annotation of 
medical images demands extensive expertise and time; and (3) Absence of 
Multi-modalities, where the need for complementary imaging insights is 
hindered by equipment limitations or patient safety concerns.

To tackle these issues, the proposed work focuses on three key objectives: 
(1) Improving the performance of tail classes in classification tasks; (2) 
Generating image pairs with their corresponding pixel-level annotations using 
advanced generative models; and (3) Translating information from existing 
modalities to predict and simulate missing ones. For the first objective, two 
approaches are introduced: one employing few-shot learning to develop a 
robust feature extractor for head classes, and the other utilizing diffusion 
models to synthesize additional samples for tail classes, thereby mitigating 
class imbalance. For the second objective, a text-to-image synthesis model, 
conditioned on segmentation masks, is proposed, incorporating an additional 
reward model to enhance alignment between text and generated images. The 
third objective addresses the modalities of Optical Coherence Tomography 
(OCT) and OCT Angiography (OCTA), where the latter provides information on 
retinal blood flow but requires expensive equipment. We propose an 
OCT-to-OCTA modality translation method to generate high-quality OCTA images 
from OCT data.

Through comprehensive experiments and analyses, this thesis presents 
potential solutions to alleviate data scarcity challenges in medical imaging, 
contributing to the advancement of these critical tasks.


Date:                   Thursday, 16 January 2025

Time:                   1:30pm - 3:30pm

Venue:                  Room 3494
                        Lifts 25/26

Chairman:               Prof. Minhua SHAO (CBE)

Committee Members:      Prof. Tim CHEN (Supervisor)
                        Prof. Albert CHUNG
                        Dr. Long CHEN
                        Dr. Fengbin TU (ECE)
                        Prof. Antoni Bert Chan (CityU)