Generative Adversarial Networks for Stain Normalisation in Histopathology
This is an incremental review aimed at researchers and practitioners in digital pathology to improve AI model robustness for clinical diagnoses.
The paper reviews stain normalization techniques in digital pathology to address visual variability that hinders AI model generalization, noting that GAN-based methods often outperform non-generative approaches but with higher computational costs and no clear best method across all scenarios.
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.