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 attacks and defenses for medical image analysis with a systematic taxonomy in terms of the application scenario. We also provide a unified framework for different types of adversarial attack and defense methods in the context of 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. Code is available on GitHub.

中文翻译：

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医学图像分析对抗性攻防研究综述：方法与挑战


深度学习技术在计算机辅助医学图像分析中取得了卓越的性能，但它们仍然容易受到难以察觉的对抗性攻击，从而导致临床实践中的潜在误诊。相反，近年来在深度医疗诊断系统中防御这些定制的对抗性示例方面也取得了显着进展。在本论述中，我们全面调查了医学图像分析的对抗性攻击和防御的最新进展，并根据应用场景进行了系统的分类。我们还为医学图像分析背景下不同类型的对抗性攻击和防御方法提供了一个统一的框架。为了进行公平的比较，我们为在各种情况下通过对抗性训练获得的对抗鲁棒医学诊断模型建立了一个新的基准。据我们所知，这是第一篇对对抗稳健的医学诊断模型进行全面评估的调查论文。通过分析定性和定量结果，我们总结了这项调查，详细讨论了医学图像分析系统中对抗性攻击和防御的当前挑战，以阐明未来的研究方向。GitHub 上提供了代码。