Stain consistency learning: handling stain variation for automatic digital pathology segmentation
File(s)
Published version (early access)
Author(s)
Type
Journal Article
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 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. Code is available at:https://github.com/mlyg/stain_consistency_learning.
Date Issued
2026-04-23
Date Acceptance
2026-04-18
Citation
IEEE Open Journal of Engineering in Medicine and Biology, 2026
ISSN
2644-1276
Publisher
IEEE
Journal / Book Title
IEEE Open Journal of Engineering in Medicine and Biology
Copyright Statement
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
License URL
Identifier
10.1109/OJEMB.2026.3687108
Subjects
Computational pathology
domain adaptation
instance segmentation
stain augmentation
stain normalization Impact Statement-Stain Consistency Learning
achieving state-of-the-art segmentation performance without extra inference-time cost
Publication Status
Published online
Date Publish Online
2026-04-23