Generative Adversarial Networks for Stain Normalisation in Histopathology

TL;DR

This paper reviews the use of generative adversarial networks (GANs) for stain normalisation in digital pathology, highlighting their advantages, challenges, and the ongoing search for the most effective approach to improve AI model robustness.

Contribution

It provides a comprehensive overview of GAN-based stain normalisation techniques and discusses their comparative performance and computational demands.

Findings

GAN methods generally outperform non-GAN approaches

No single method is best; performance varies by scenario

GANs require higher computational resources

Abstract

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.