  Generative adversarial networks (GANs) are unsupervised Deep Learning
approach in the computer vision community which has gained significant
attention from the last few years in identifying the internal structure of
multimodal medical imaging data. The adversarial network simultaneously
generates realistic medical images and corresponding annotations, which proven
to be useful in many cases such as image augmentation, image registration,
medical image generation, image reconstruction, and image-to-image translation.
These properties bring the attention of the researcher in the field of medical
image analysis and we are witness of rapid adaption in many novel and
traditional applications. This chapter provides state-of-the-art progress in
GANs-based clinical application in medical image generation, and cross-modality
synthesis. The various framework of GANs which gained popularity in the
interpretation of medical images, such as Deep Convolutional GAN (DCGAN),
Laplacian GAN (LAPGAN), pix2pix, CycleGAN, and unsupervised image-to-image
translation model (UNIT), continue to improve their performance by
incorporating additional hybrid architecture, has been discussed. Further, some
of the recent applications of these frameworks for image reconstruction, and
synthesis, and future research directions in the area have been covered.
