  Unmasking the decision-making process of machine learning models is essential
for implementing diagnostic support systems in clinical practice. Here, we
demonstrate that adversarially trained models can significantly enhance the
usability of pathology detection as compared to their standard counterparts. We
let six experienced radiologists rate the interpretability of saliency maps in
datasets of X-rays, computed tomography, and magnetic resonance imaging scans.
Significant improvements were found for our adversarial models, which could be
further improved by the application of dual batch normalization. Contrary to
previous research on adversarially trained models, we found that the accuracy
of such models was equal to standard models when sufficiently large datasets
and dual batch norm training were used. To ensure transferability, we
additionally validated our results on an external test set of 22,433 X-rays.
These findings elucidate that different paths for adversarial and real images
are needed during training to achieve state of the art results with superior
clinical interpretability.
