BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Gitta Kutyniok (LMU Munich) DTSTART:20210427T101500Z DTEND:20210427T114500Z DTSTAMP:20260423T022603Z UID:MathDeep/2 DESCRIPTION:Title: Deep Learning meets Shearlets: On the Path Towards Interpretable Imaging< /a>\nby Gitta Kutyniok (LMU Munich) as part of Mathematics of Deep Learnin g\n\n\nAbstract\nPure model-based approaches are today often insufficient for solving complex inverse problems in medical imaging. At the same time\ , methods based on artificial intelligence\, in particular\, deep neural n etworks\, are extremely successful\, often quickly leading to state-of-the -art algorithms. However\, pure deep learning approaches often neglect kno wn and valuable information from the modeling world and suffer from a lack of interpretability.\n\nIn this talk\, we will develop a conceptual appro ach by combining the model-based method of sparse regularization by shearl ets with the data-driven method of deep learning. Our solvers pay particul ar attention to the singularity structures of the data. Focussing then on the inverse problem of (limited-angle) computed tomography\, we will show that our algorithms significantly outperform previous methodologies\, incl uding methods entirely based on deep learning. Finally\, we will also touc h upon the issue of how to interpret such algorithms\, and present a novel \, state-of-the-art explainability method based on information theory.\n LOCATION:https://researchseminars.org/talk/MathDeep/2/ END:VEVENT END:VCALENDAR