Generative Adversarial Networks for Deep Learning in Healthcare: Architecture, Applications and Challenges

Deep Learning is a new generation of artificial neural networks that have transformed our daily lives and has impacted several industries and scientific disciplines in recent years. Recent development in deep learning provides various significant methods to obtain end-to-end learning models from complex data. Generative Adversarial Networks give the idea of learning the deep representations without extensively interpreting the training data. The generative adversarial network involves the generative modeling approach that uses the deep learning approach. The chapter is broadly divided into the following sections as (1) Insights of deep learning & the generative adversarial networks, (2) GAN’s representative variants, training methods, architecture, and mathematical representation, and (3) Efficacy of GAN in different applications. This chapter will gain the recent work done in GAN and implement this technique in the different deep learning applications for healthcare. Here, we will also analyze some of the future perspectives and trends in the forthcoming time.

Keywords: Deep Learning, Generative Adversarial Networks, Healthcare.