  Variability in staining protocols, such as different slide preparation
techniques, chemicals, and scanner configurations, can result in a diverse set
of whole slide images (WSIs). This distribution shift can negatively impact the
performance of deep learning models on unseen samples, presenting a significant
challenge for developing new computational pathology applications. In this
study, we propose a method for improving the generalizability of convolutional
neural networks (CNNs) to stain changes in a single-source setting for semantic
segmentation. Recent studies indicate that style features mainly exist as
covariances in earlier network layers. We design a channel attention mechanism
based on these findings that detects stain-specific features and modify the
previously proposed stain-invariant training scheme. We reweigh the outputs of
earlier layers and pass them to the stain-adversarial training branch. We
evaluate our method on multi-center, multi-stain datasets and demonstrate its
effectiveness through interpretability analysis. Our approach achieves
substantial improvements over baselines and competitive performance compared to
other methods, as measured by various evaluation metrics. We also show that
combining our method with stain augmentation leads to mutually beneficial
results and outperforms other techniques. Overall, our study makes significant
contributions to the field of computational pathology.
