Title: Denoising Diffusion Models for Medical Image Analysis

Speaker: Julia Wolleb

Abstract:
Over the past two years, denoising diffusion models for image generation have seen tremendous success. This new class of deep learning models outperforms previous approaches and has become widely popular with frameworks such as Stable Diffusion and Dall-E for text-to-image generation. We explore how this state-of-the-art technique can be applied to medical tasks. We will discuss medical applications such as segmentation of anatomical structures, contrast harmonization of MR images, automatic implant generation, and weakly supervised anomaly detection. Additionally, the presentation will provide insights into the current state of research, highlight limitations, and offer a glimpse of future directions in this field.

Speaker Bio:
Julia Wolleb is a postdoctoral researcher at the Center for medical Image Analysis & Navigation at the University of Basel. She holds a Master's degree in Mathematics from the University of Basel, with a focus on numerics and algebra. She completed her Master's thesis in 2018 in the Mathematical Epidemiology group at the Swiss Tropical and Public Health Institute. She then pursued a PhD at the Department of Biomedical Engineering at the University of Basel, where she successfully defended her thesis in 2022, which mainly focused on the automatic detection of pathological regions in medical images. Julia's research interests focus on the development of robust and reliable deep learning methods for medical image analysis in clinical applications. Throughout her academic career, she has explored various deep learning approaches, including tasks such as image segmentation, weakly supervised anomaly detection, image-to-image translation, and domain adaptation.

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