Title: Generative Diffusion Models for Medical Imaging
Speaker: Hyungjin Chung
Abstract:
Foundational generative models are gaining more and more interest in recent days. Among them, all the modalities except language seem to be converging towards a single class of model: diffusion models. In this talk, we will focus on leveraging diffusion models as generative priors using Bayesian inference, and how to use them to solve inverse problems, especially focusing on medical imaging. The talk will be focused on fully leveraging the power of foundation models by using them as plug-and-play building blocks to solve challenging downstream tasks. At the end of the talk, a new work on adapting diffusion models for out-of-distribution measurements, showcasing that diffusion models can be used to reconstruct data that were trained on completely different datasets.
Speaker Bio:
Hyungjin Chung is a PhD student at KAIST, and a student researcher at Google AI. His research interests lie on the intersection between deep generative models and computational imaging. Especially, he has pioneered many works on using diffusion models for solving inverse problems, many of them focusing on biomedical imaging. Hyungjin completed his Master's degree in KAIST, and his Bachelor's degree in Korea University.
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