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.

本文介绍了在医学影像诊断中应用深度学习的机会与挑战，重点讲述了可解释人工智能（XAI）在医学影像中的应用，并对解释方法、数据集和评估指标进行了综述和比较，最后探讨了深度学习在医学影像领域的未来研究方向。