  Remarkable successes were made in Medical Image Classification (MIC)
recently, mainly due to wide applications of convolutional neural networks
(CNNs). However, adversarial examples (AEs) exhibited imperceptible similarity
with raw data, raising serious concerns on network robustness. Although
adversarial training (AT), in responding to malevolent AEs, was recognized as
an effective approach to improve robustness, it was challenging to overcome
generalization decline of networks caused by the AT. In this paper, in order to
reserve high generalization while improving robustness, we proposed a dynamic
perturbation-adaptive adversarial training (DPAAT) method, which placed AT in a
dynamic learning environment to generate adaptive data-level perturbations and
provided a dynamically updated criterion by loss information collections to
handle the disadvantage of fixed perturbation sizes in conventional AT methods
and the dependence on external transference. Comprehensive testing on
dermatology HAM10000 dataset showed that the DPAAT not only achieved better
robustness improvement and generalization preservation but also significantly
enhanced mean average precision and interpretability on various CNNs,
indicating its great potential as a generic adversarial training method on the
MIC.
