  Recently, deep neural networks (DNNs) have made great progress on automated
diagnosis with chest X-rays images. However, DNNs are vulnerable to adversarial
examples, which may cause misdiagnoses to patients when applying the DNN based
methods in disease detection. Recently, there is few comprehensive studies
exploring the influence of attack and defense methods on disease detection,
especially for the multi-label classification problem. In this paper, we aim to
review various adversarial attack and defense methods on chest X-rays. First,
the motivations and the mathematical representations of attack and defense
methods are introduced in details. Second, we evaluate the influence of several
state-of-the-art attack and defense methods for common thorax disease
classification in chest X-rays. We found that the attack and defense methods
have poor performance with excessive iterations and large perturbations. To
address this, we propose a new defense method that is robust to different
degrees of perturbations. This study could provide new insights into
methodological development for the community.
