  Image analysis in the field of digital pathology has recently gained
increased popularity. The use of high-quality whole slide scanners enables the
fast acquisition of large amounts of image data, showing extensive context and
microscopic detail at the same time. Simultaneously, novel machine learning
algorithms have boosted the performance of image analysis approaches. In this
paper, we focus on a particularly powerful class of architectures, called
Generative Adversarial Networks (GANs), applied to histological image data.
Besides improving performance, GANs also enable application scenarios in this
field, which were previously intractable. However, GANs could exhibit a
potential for introducing bias. Hereby, we summarize the recent
state-of-the-art developments in a generalizing notation, present the main
applications of GANs and give an outlook of some chosen promising approaches
and their possible future applications. In addition, we identify currently
unavailable methods with potential for future applications.
