  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.
