Recent advancements in deep learning (DL) have significantly advanced medical image analysis. In the field of medical image processing, particularly in histopathology image analysis, the variation in staining protocols and differences in scanners present significant domain shift challenges, undermine the generalization capabilities of models to the data from unseen domains, prompting the need for effective domain generalization (DG) strategies to improve the consistency and reliability of automated cancer detection tools in diagnostic decision-making. In this paper, we introduce Pathology Weight Averaging (PathoWAve), a multi-source DG strategy for addressing domain shift phenomenon of DL models in histopathology image analysis. Integrating specific weight averaging technique with parallel training trajectories and a strategically combination of regular augmentations with histopathology-specific data augmentation methods, PathoWAve enables a comprehensive exploration and precise convergence within the loss landscape. This method significantly enhanced generalization capabilities of DL models across new, unseen histopathology domains. To the best of our knowledge, PathoWAve is the first proposed weight averaging method for DG in histopathology image analysis. Our quantitative results on Camelyon17 WILDS dataset demonstrate PathoWAve's superiority over previous proposed methods to tackle the domain shift phenomenon in histopathology image processing. Our code is available at \url{https://github.com/ParastooSotoudeh/PathoWAve}.

在医学图像处理领域中，深度学习取得了显著突破，但染色协议的差异和扫描仪的不同导致领域偏移问题，影响了模型对未见领域数据的泛化能力。本文介绍了一种名为PathoWAve的多源领域泛化策略，它通过特定的权重平均技术、平行训练轨迹和组合增强方法，显著提高了深度学习模型在未见组织病理学领域的泛化性能，在Camelyon17 WILDS数据集上取得了优于先前方法的结果。

PathoWAve：一种基于深度学习的加权平均方法，用于改善组织病理学图像中的领域泛化