PathoWAve：一种用于提升组织病理学图像域泛化能力的基于深度学习的权重平均方法
PathoWAve: A Deep Learning-based Weight Averaging Method for Improving Domain Generalization in Histopathology Images
AI总结：
针对组织病理学图像因染色、扫描仪差异导致的域偏移问题，提出多源域泛化方法PathoWAve，结合权重平均、并行训练与专属数据增强，在Camelyon17 WILDS数据集上表现优于现有方法。
摘要：
深度学习（DL）的最新进展极大推动了医学图像分析的发展。在医学图像处理领域，尤其是组织病理学图像分析中，染色方案的差异和扫描仪的不同带来了显著的域偏移挑战，削弱了模型对未见过域数据的泛化能力，因此需要有效的域泛化（DG）策略来提升自动化癌症检测工具在诊断决策中的一致性与可靠性。本文提出病理权重平均法（PathoWAve），这是一种多源域泛化策略，用于解决组织病理学图像分析中深度学习模型的域偏移现象。PathoWAve将特定权重平均技术与并行训练轨迹相结合，同时策略性地融合常规增强与组织病理学专属数据增强方法，能够在损失景观中实现全面探索与精准收敛。该方法显著提升了深度学习模型在全新的、未见过的组织病理学域中的泛化能力。据我们所知，PathoWAve是首个针对组织病理学图像分析域泛化提出的权重平均方法。我们在Camelyon17 WILDS数据集上的定量结果表明，PathoWAve的性能优于此前提出的应对组织病理学图像处理中域偏移现象的方法。我们的代码可在https://github.com/ParastooSotoudeh/PathoWAve 获取。
英文摘要：
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}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
