Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation
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 Massons trichrome and H&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.
Fields of Study & Subjects
AI in cancer detection
Digital Imaging for Blood Diseases
Generative Adversarial Networks and Image Synthesis
Publication details
- Journal / source
- IEEE Open Journal of Engineering in Medicine and Biology
- Volume
- 7
- Pages
- 187–195
- Publication date
- 1 January 2026
- Language
- EN