论文标题
医学图像分类中的可解释深度学习方法：调查
Explainable Deep Learning Methods in Medical Image Classification: A Survey
论文作者
论文摘要
深度学习的显着成功引起了人们对医学成像诊断的应用的兴趣。尽管最新的深度学习模型在分类的不同类型的医学数据方面已经达到了人类水平的准确性，但这些模型在临床工作流程中几乎不采用，这主要是由于它们缺乏可解释性。深度学习模型的黑盒子性提出了制定策略来解释这些模型的决策过程的必要性，从而导致了可解释的人工智能（XAI）主题的创建。在这种情况下，我们对应用于医学成像诊断的XAI进行了详尽的调查，包括视觉，基于示例和基于概念的解释方法。此外，这项工作回顾了现有的医学成像数据集和现有指标，以评估解释的质量。此外，我们还包括一组基于报告生成的方法的性能比较。最后，还讨论了将XAI应用于医学成像以及有关该主题的未来研究指示的主要挑战。
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.
