Diffusion Models For Medical Imaging
I recently gave a keynote at MICAD 2022 on Diffusion Models in Medical Imaging and Analysis. Hype or Hope?
Abstract: Generative models, such as VAEs, GANs, Normalising Flows, have been extremely useful in medical imaging and analysis for finding useful representation spaces, creating additional unseen examples, or simply acting as regularisers within a multitask learning setting. A new breed of models is now receiving considerable attention in AI and Computer Vision. Diffusion models are now empowering famous examples of massive multimodal (text/image) generative models such as Stable Diffusion (Stability.AI), ImageGen (Google), Dall-E (OpenAI). But are they full of hype or hope? And what is their role in medical imaging and analysis? In this talk we will briefly recap the theory of diffusion models, summarise recent papers that use diffusion models in medical imaging and analysis, and offer inspiring papers from computer vision to help guide future directions in the use of these models in our field. We conclude that such models are far from being …!
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