Deep learning techniques have achieved superior performance in computer-aided medical image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in potential misdiagnosis in clinical practice. Oppositely, recent years have also witnessed remarkable progress in defense against these tailored adversarial examples in deep medical diagnosis systems. In this exposition, we present a comprehensive survey on recent advances in adversarial attack and defense for medical image analysis with a novel taxonomy in terms of the application scenario. We also provide a unified theoretical framework for different types of adversarial attack and defense methods for medical image analysis. For a fair comparison, we establish a new benchmark for adversarially robust medical diagnosis models obtained by adversarial training under various scenarios. To the best of our knowledge, this is the first survey paper that provides a thorough evaluation of adversarially robust medical diagnosis models. By analyzing qualitative and quantitative results, we conclude this survey with a detailed discussion of current challenges for adversarial attack and defense in medical image analysis systems to shed light on future research directions.

翻译：深度学习技术在计算机辅助医学图像分析中已取得卓越性能，但这类方法仍易受难以察觉的对抗攻击影响，可能导致临床实践中出现误诊。相反，近年来针对深度医疗诊断系统中这些定制化对抗样本的防御研究也取得了显著进展。本文以应用场景为新型分类维度，全面综述了医学图像分析领域对抗攻击与防御的最新进展。我们为医学图像分析中不同类型的对抗攻击与防御方法建立了统一的理论框架。为进行公平比较，我们针对不同场景下通过对抗训练获得的鲁棒医疗诊断模型建立了新基准。据我们所知，这是首篇对鲁棒医疗诊断模型进行系统评估的综述论文。通过定性与定量分析结果，我们最终以对当前医学图像分析系统中对抗攻击与防御挑战的详细讨论作为总结，为未来研究方向提供启示。