  The rapid growth of digital pathology in recent years has provided an ideal
opportunity for the development of artificial intelligence-based tools to
improve the accuracy and efficiency of clinical diagnoses. One of the
significant roadblocks to current research is the high level of visual
variability across digital pathology images, causing models to generalise
poorly to unseen data. Stain normalisation aims to standardise the visual
profile of digital pathology images without changing the structural content of
the images. In this chapter, we explore different techniques which have been
used for stain normalisation in digital pathology, with a focus on approaches
which utilise generative adversarial networks (GANs). Typically, GAN-based
methods outperform non-generative approaches but at the cost of much greater
computational requirements. However, it is not clear which method is best for
stain normalisation in general, with different GAN and non-GAN approaches
outperforming each other in different scenarios and according to different
performance metrics. This is an ongoing field of study as researchers aim to
identify a method which efficiently and effectively normalises pathology images
to make AI models more robust and generalisable.
