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SUMMARY:Computationally efficient data-driven solutions to inverse problem
 s in X-ray CT - Subhadip Mukherjee (University of Bath)
DTSTART:20230203T110000Z
DTEND:20230203T114500Z
UID:TALK194575@talks.cam.ac.uk
DESCRIPTION:Inverse problems arise frequently in medical imaging applicati
 ons\, for instance in X-ray computed tomography (CT)\, where the goal is t
 o recover the interior structural details of an underlying object from its
  noisy and potentially undersampled measurement. In recent years\, deep le
 arning has proved to be a transformative tool for imaging inverse problems
 \, leading to objectively better reconstruction as compared to the classic
 al variational framework. The talk will give a brief overview of the impor
 tant deep learning-based approaches for image recovery (especially in the 
 context of X-ray CT) while highlighting their relative merits and demerits
 . The key focus of the talk will be on the computational efficiency of dat
 a-driven methods\, and we will discuss some of our recent contributions to
  developing fast learning-based approaches using ideas such as adversarial
  learning and unrolling stochastic optimization methods.
LOCATION:Seminar Room 1\, Newton Institute
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