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.

本研究提出了一种卷积神经网络的通道注意机制，用于检测特定染色的特征，并对先前提出的耐染色训练方案进行修改，以提高单一来源的卷积神经网络对染色变化的普适性。通过在多中心和多染色样本集上的评估，我们证明了该方法的有效性，并表明该方法和染色增强的组合可以实现相互有益的结果。