Stain consistency learning: handling stain variation for automatic digital pathology segmentation - Yeung,M; Watts,T; Tan,SYW; Jing,P; Ferreira,PF; Scott,AD; Nielles-Vallespin,S; Yang,G (IEEE Open Journal of Engineering in Medicine and Biology, 2026)
Abstract—Stain variation poses a major challenge for automated digital pathology. Numerous techniques address this issue, yet show limited success, especially outside H&E stains and classification tasks. We propose Stain Consistency Learning (SCL), combining stain-specific augmentation and a novel consistency loss to learn stain-invariant features. We conduct the first large scale evaluation of ten methods on Masson’s trichrome andH&E datasets for segmentation. Our results demonstrate that traditional stain normalization offers little benefit, while stain augmentation and adversarial learning significantly improve performance. SCL consistently outperforms all other methods.