The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning-based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field.

中文翻译：

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人工智能在组织病理学图像分析中的应用

组织病理学中越来越多地采用全幻灯片图像 (WSI) 技术极大地改变了病理学家的工作流程，并允许在组织病理学分析中使用计算机系统。人工智能 (AI) 的广泛研究取得了巨大进展，从而为癌症诊断、预后和治疗等多种应用提供了高效、有效和稳健的算法。这些算法提供高度准确的预测，但缺乏透明度、可理解性和可操作性。因此，可解释的人工智能 (XAI) 技术不仅需要了解 AI 方法做出的决策背后的机制并增加用户信任，而且还需要扩大 AI 算法在临床环境中的使用。根据对 150 多篇论文的调查，我们探索了已应用并有助于组织病理学图像分析工作流程的不同 AI 算法。我们首先解决组织病理学过程的工作流程。我们概述了与组织病理学成像中的深度学习方法相关的各种基于学习的 XAI 和可操作技术。我们还解决了 XAI 方法的评估以及确保它们在现场的可靠性的需要。