O. Öktem (KTH Royal Institute of Technology)
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The talk will outline recent approaches for using (deep) convolutional neural
networks to solve a wide range of inverse problems, such as image
reconstruction in medical tomography. A key element is to use a neural network
architecture for reconstruction that includes physics based models that
describe how data is generated as well as its statistical properties  Another
is the possibility to integrate complex task related a priori information and
elements of decision making into the reconstruction procedure. The resulting
approach outperforms current state-of-the-art in terms of 'quality',
computational speed and there is no need to manually set parameters as with
variational methods. Furthermore, the amount of training data and network size
can be kept surprisingly small.