Computer Science & Engineering Building (CSE), Room 2154

In this talk I will discuss the challenges and opportunities for using deep learning in medical image reconstruction. Contemporary techniques in this field rely on convolutional architectures that are limited by the spatial invariance of their filters and have difficulty modeling long-range dependencies. To remedy this, I will discuss our work on designing new transformer-based architectures called HUMUS-Net that lead to state of the art performance and do not suffer from these limitations. In the next part of the talk I will report on techniques to significantly reduce the required data for training. Finally, I will briefly discuss our recent attempts to develop rigorous theory for simple end-to-end training methods used in image reconstruction problems which is surprisingly quite challenging even for simple target functions. Notability, our theory will be in the rich (or beyond NTK regime) that conforms with practical choice of hyperparameters. Time permitting I will discuss other exciting directions for the use of deep learning in MR.