The utility of color normalization for AI-based diagnosis of hematoxylin and eosin-stained pathology images

The color variation of hematoxylin and eosin (H&E)-stained tissues has presented a challenge for applications of artificial intelligence (AI) in digital pathology. Many color normalization algorithms have been developed in recent years in order to reduce the color variation between H&E images. However, previous efforts in benchmarking these algorithms have produced conflicting results and none have sufficiently assessed the efficacy of the various color normalization methods for improving diagnostic performance of AI systems. In this study, we systematically investigated eight color normalization algorithms for AI-based classification of H&E-stained histopathology slides, in the context of using images both from one center and from multiple centers. Our results show that color normalization does not consistently improve classification performance when both training and testing data are from a single center. However, using four multi-center datasets of two cancer types (ovarian and pleural) and objective functions, we show that color normalization can significantly improve the classification accuracy of images from external datasets (ovarian cancer: 0.25 AUC increase, p = 1.6 e-05; pleural cancer: 0.21 AUC increase, p = 1.4 e-10). Furthermore, we introduce a novel augmentation strategy by mixing color-normalized images using three easily accessible algorithms that consistently improves the diagnosis of test images from external centers, even when the individual normalization methods had varied results. We anticipate our study to be a starting point for reliable use of color normalization to improve AI-based, digital pathology-empowered diagnosis of cancers sourced from multiple centers. (c) 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Automated summary

This study compared eight color normalization algorithms for AI-based classification of H&E-stained histopathology slides, finding that while color normalization does not consistently improve classification on single-center data, it significantly enhances accuracy on multi-center datasets. Introducing a novel augmentation strategy by mixing color-normalized images using three algorithms consistently improves diagnosis of test images from external centers, showing the potential for reliable color normalization in AI-based digital pathology diagnosis of cancers from multiple centers.