Explore deep learning approaches for medical image processing in this 49-minute lecture by Daniel Rueckert from Imperial College London. Delve into techniques for reconstructing, enhancing resolution, and segmenting Magnetic Resonance (MR) images using advanced deep learning methods. Learn how to incorporate anatomical shape information as prior knowledge to improve these processes. Discover the potential of using shape and motion data to develop interpretable deep learning models for diagnosis and prognosis. This talk, part of the Deep Learning and Medical Applications 2020 series at UCLA's Institute for Pure and Applied Mathematics, offers valuable insights into the intersection of artificial intelligence and medical imaging.
Deep Learning and Shape Modelling for Medical Image Reconstruction, Segmentation and Analysis
Institute for Pure & Applied Mathematics (IPAM) via YouTube
Overview
Syllabus
Daniel Rueckert: "Deep learning and shape modelling for medical image reconstruction, segmentati..."
Taught by
Institute for Pure & Applied Mathematics (IPAM)
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