Generative adversarial networks for histopathology staining

Abstract Histology and histopathology provide the gold standard for diagnosticians to understand the underlying relationship between the structures on a tissue sample and the affliction of the subject from whom the tissue was taken. A fundamental step toward achieving this is staining the tissue with chemicals to accentuate features within it. Over the past one-and-a-half centuries many stains have been developed, with each stain providing its own unique set of features. Very often, obtaining multiple slides with diverse stains is a cumbersome process that is both expensive and prone to human error. The objective of this chapter is to treat the issue of obtaining the image of a stained tissue from a differently stained one as an image-to-image translation problem. The solution is based on a suitably chosen conditional generative adversarial network, which transforms an image from one feature space to another. This chapter discusses the choice of objective functions and image-quality metrics. In addition, it highlights some of the issues with suggestions to address them.