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Title: Label-efficient Deep Learning for 3D Medical Image Analysis: Self-supervised and Generative Approaches

Presenter: Li Sun

Advisor: Professor Kayhan Batmanghelich

Chair: Professor Archana Venkataraman,

Committee: Professor Kayhan Batmanghelich, Professor Archana Venkataraman, Professor Ioannis Paschalidis.

Abstract: Deep learning methods have achieved state-of-the-art performance in various tasks of medical image analysis. However, the success relies heavily on the expensive and time-consuming collection of large quantities of labeled data, which is not always available. Compared to natural images, medical images have domain-specific anatomical context, which is less explored in previous research. In this work, we explore self-supervised and generative methods to improve the label efficiency of deep learning methods for 3D medical image analysis. This prospectus contains three prongs. First, we introduce a two-stage contrastive framework that learns fine-grained disease-related representation by leveraging anatomical context. Further, while previous methods are constrained by the available memory in terms of generation resolution, we propose a hierarchical GAN that can generate high-resolution 3D images for data augmentation. For future work, we will explore learning location-sensitive representation and develop a generative diffusion model that leverages anatomical prior.