Abstract

Co-Author: Jonas Adler 

Machine learning has been used if image reconstruction for several years, mostly driven by the recent advent of deep learning. Deep learning based reconstruction methods have been shown to give good reconstruction quality by learning a reconstruction operator that maps data directly to reconstruction. These methods typically perform very well when performance is measured using classical quantified, such as the RMSE , but they tend to produce over-smoothed images, reducing their usefulness in applications.  

We propose a framework based on statistical decision theory that allows learning a reconstruction operator that is optimal with respect to a given task, which  can be segmentation of a tumor or classification. In this framework, deep learning is used not only to solve the inverse problem, but also to simultaneously learn how to use the reconstructed image in order to complete an end-task. We demonstrate that the framework is computationally feasible and that it can improve human interpretability of the reconstructions. We also suggest new research directions in the field of data driven, task oriented image reconstruction.  

Related publications:
http://arxiv.org/abs/1704.04058 (accepted for publication in Inverse Problems)
http://arxiv.org/abs/1707.06474 (submitted to IEEE Transaction for Medical Imaging)