  It is known that Deep Neural networks (DNNs) are vulnerable to adversarial
attacks, and the adversarial robustness of DNNs could be improved by adding
adversarial noises to training data (e.g., the standard adversarial training
(SAT)). However, inappropriate noises added to training data may reduce a
model's performance, which is termed the trade-off between accuracy and
robustness. This problem has been sufficiently studied for the classification
of whole images but has rarely been explored for image analysis tasks in the
medical application domain, including image segmentation, landmark detection,
and object detection tasks. In this study, we show that, for those medical
image analysis tasks, the SAT method has a severe issue that limits its
practical use: it generates a fixed and unified level of noise for all training
samples for robust DNN training. A high noise level may lead to a large
reduction in model performance and a low noise level may not be effective in
improving robustness. To resolve this issue, we design an adaptive-margin
adversarial training (AMAT) method that generates sample-wise adaptive
adversarial noises for robust DNN training. In contrast to the existing,
classification-oriented adversarial training methods, our AMAT method uses a
loss-defined-margin strategy so that it can be applied to different tasks as
long as the loss functions are well-defined. We successfully apply our AMAT
method to state-of-the-art DNNs, using five publicly available datasets. The
experimental results demonstrate that: (1) our AMAT method can be applied to
the three seemingly different tasks in the medical image application domain;
(2) AMAT outperforms the SAT method in adversarial robustness; (3) AMAT has a
minimal reduction in prediction accuracy on clean data, compared with the SAT
method; and (4) AMAT has almost the same training time cost as SAT.
