  Generative Adversarial Networks (GANs) and their extensions have carved open
many exciting ways to tackle well known and challenging medical image analysis
problems such as medical image de-noising, reconstruction, segmentation, data
simulation, detection or classification. Furthermore, their ability to
synthesize images at unprecedented levels of realism also gives hope that the
chronic scarcity of labeled data in the medical field can be resolved with the
help of these generative models. In this review paper, a broad overview of
recent literature on GANs for medical applications is given, the shortcomings
and opportunities of the proposed methods are thoroughly discussed and
potential future work is elaborated. We review the most relevant papers
published until the submission date. For quick access, important details such
as the underlying method, datasets and performance are tabulated. An
interactive visualization which categorizes all papers to keep the review
alive, is available at
http://livingreview.in.tum.de/GANs_for_Medical_Applications.

Computer Vision and Pattern Recognition;Machine Learning;Machine Learning