Generative Adversarial Networks (GANs) are a recently introduced class of state of the art generative models. GANs are characterized by a unique training process that, although unstable, enables them to accurately learn highly complex distributions. While much of the recent attention that GANs have received in the machine learning and computer vision communities is due to their ability to synthesize highly realistic images, this is but one of many potential uses for these models. In this chapter, we survey several recent applications of GANs in medical imaging, highlighting significant developments and illustrating avenues for future work in this nascent area of research.
