Hausdorff School — "Data-driven Inverse Problems in Biomedical Imaging"

Various key problems in biomedical imaging can be modeled as inverse problems (e.g. single image super-resolution, undersampling in magnetic resonance imaging, limited angle tomography, registration, segmentation). Numerous deep learning-inspired methods to tackle these inverse problems have been developed in recent years, often redefining the state of the art. The Hausdorff School on Data-driven Inverse Problems in Biomedical Imaging focuses on several mini-courses that explore this topic from both theoretical and applied perspectives. The school is aimed at PhD students and postdocs. The courses can be supplemented by introductory lectures and shorter research lectures.