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Variability in staining protocols, such as different slide preparationtechniques, chemicals, and scanner configurations, can result in a diverse setof whole slide images (WSIs). This distribution shift can negatively impact theperformance of deep learning models on unseen samples, presenting a significantchallenge for developing new computational pathology applications. In thisstudy, we propose a method for improving the generalizability of convolutionalneural networks (CNNs) to stain changes in a single-source setting for semanticsegmentation. Recent studies indicate that style features mainly exist ascovariances in earlier network layers. We design a channel attention mechanismbased on these findings that detects stain-specific features and modify thepreviously proposed stain-invariant training scheme. We reweigh the outputs ofearlier layers and pass them to the stain-adversarial training branch. Weevaluate our method on multi-center, multi-stain datasets and demonstrate itseffectiveness through interpretability analysis. Our approach achievessubstantial improvements over baselines and competitive performance compared toother methods, as measured by various evaluation metrics. We also show thatcombining our method with stain augmentation leads to mutually beneficialresults and outperforms other techniques. Overall, our study makes significantcontributions to the field of computational pathology.

Improving Stain Invariance of CNNs for Segmentation by Fusing Channel Attention and Domain-Adversarial Training