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.

翻译：深度学习的显著成功激发了其在医学影像诊断中的应用兴趣。尽管最先进的深度学习模型在各类医学数据分类任务中已达到人类水平的准确率，但这类模型在临床工作流程中却难以落地，主要归因于其缺乏可解释性。深度学习模型的"黑箱"特性催生了设计策略来解释模型决策过程的需求，由此诞生了可解释人工智能（XAI）这一研究课题。在此背景下，本文对XAI在医学影像诊断中的应用进行了全面综述，涵盖视觉解释、文本解释、示例解释和概念解释等方法。此外，本研究回顾了现有医学影像数据集以及评估解释质量的现有指标。同时，本文对一组基于报告生成的方法进行了性能对比。最后，本文还讨论了XAI应用于医学影像面临的主要挑战及该领域的未来研究方向。