计算病理学中的领域泛化：综述与指南
Domain Generalization in Computational Pathology: Survey and Guidelines
AI总结：
本文系统综述了计算病理学中的领域泛化问题，定义了领域偏移类型，分类现有方法，并通过28种算法基准实验提出检测与管理领域偏移的实用指南。
摘要：
深度学习模型通过处理一系列组织学图像分析应用中的复杂任务，已在计算病理学（CPath）中展现出卓越的有效性。然而，分布外数据的存在（源于多种来源，如不同的成像设备和多样的组织制备方法）会导致领域偏移（DS）。DS 会降低已训练模型对数据分布略有不同的未见数据集的泛化能力，从而催生了对创新性领域泛化（DG）解决方案的需求。认识到 DG 方法在癌症研究和临床实践中可能对诊断和预后模型产生重大影响，我们在此提供本综述以及关于在 CPath 中实现 DG 的指南。我们严格定义了各种 DS 类型，系统回顾并分类了 CPath 中现有的 DG 方法和资源，并对其优势、局限性和适用性提供了见解。我们还使用 28 种前沿 DG 算法进行了全面的基准测试实验，以解决一个复杂的 DG 问题。我们的研究结果表明，谨慎的实验设计和 CPath 特异性的染色增强技术可能非常有效。然而，对于 CPath 中的 DG，并不存在放之四海而皆准的解决方案。因此，我们建立了明确的指南，用于根据不同场景检测和管理 DS。虽然大多数概念、指南和建议是针对 CPath 应用给出的，但我们相信它们同样适用于大多数医学图像分析任务。
英文摘要：
Deep learning models have exhibited exceptional effectiveness in Computational Pathology (CPath) by tackling intricate tasks across an array of histology image analysis applications. Nevertheless, the presence of out-of-distribution data (stemming from a multitude of sources such as disparate imaging devices and diverse tissue preparation methods) can cause \emph{domain shift} (DS). DS decreases the generalization of trained models to unseen datasets with slightly different data distributions, prompting the need for innovative \emph{domain generalization} (DG) solutions. Recognizing the potential of DG methods to significantly influence diagnostic and prognostic models in cancer studies and clinical practice, we present this survey along with guidelines on achieving DG in CPath. We rigorously define various DS types, systematically review and categorize existing DG approaches and resources in CPath, and provide insights into their advantages, limitations, and applicability. We also conduct thorough benchmarking experiments with 28 cutting-edge DG algorithms to address a complex DG problem. Our findings suggest that careful experiment design and CPath-specific Stain Augmentation technique can be very effective. However, there is no one-size-fits-all solution for DG in CPath. Therefore, we establish clear guidelines for detecting and managing DS depending on different scenarios. While most of the concepts, guidelines, and recommendations are given for applications in CPath, we believe that they are applicable to most medical image analysis tasks as well.
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