医学图像分类中的可解释深度学习方法：综述
Explainable Deep Learning Methods in Medical Image Classification: A Survey
- University of Beira Interior（贝拉内格罗大学）
- NOVA LINCS（新里斯本大学计算机科学与信息学实验室）
- University of Porto（波尔图大学）
- INESC TEC（葡萄牙系统与计算机工程、技术与科学研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文综述了可解释人工智能在医学影像诊断中的应用，系统梳理了可视化、文本、示例和概念等解释方法、数据集与评估指标，并比较了报告生成方法性能，指出主要挑战与未来方向。
AI中文摘要：
深度学习的显著成功引发了人们对其在医学影像诊断中应用的兴趣。尽管最先进的深度学习模型已在不同类型医学数据的分类上达到了人类水平的准确率，但这些模型很少被用于临床工作流程，主要原因是缺乏可解释性。深度学习模型的黑箱特性提出了设计策略来解释这些模型决策过程的需求，从而催生了可解释人工智能（XAI）这一主题。在此背景下，我们对应用于医学影像诊断的XAI进行了全面综述，包括可视化、文本、基于示例和基于概念的解释方法。此外，本文回顾了现有的医学影像数据集以及用于评估解释质量的现有指标。另外，我们还对一组基于报告生成的方法进行了性能比较。最后，本文还讨论了将XAI应用于医学影像的主要挑战以及该主题的未来研究方向。
英文摘要：
The remarkable success of deep learning has prompted interest in its application to medical imaging diagnosis. Even though state-of-the-art deep learning models have achieved human-level accuracy on the classification of different types of medical data, these models are hardly adopted in clinical workflows, mainly due to their lack of interpretability. The black-box-ness of deep learning models has raised the need for devising strategies to explain the decision process of these models, leading to the creation of the topic of eXplainable Artificial Intelligence (XAI). In this context, we provide a thorough survey of XAI applied to medical imaging diagnosis, including visual, textual, example-based and concept-based explanation methods. Moreover, this work reviews the existing medical imaging datasets and the existing metrics for evaluating the quality of the explanations. In addition, we include a performance comparison among a set of report generation-based methods. Finally, the major challenges in applying XAI to medical imaging and the future research directions on the topic are also discussed.
