  Explaining decisions of black-box classifiers is paramount in sensitive
domains such as medical imaging since clinicians confidence is necessary for
adoption. Various explanation approaches have been proposed, among which
perturbation based approaches are very promising. Within this class of methods,
we leverage a learning framework to produce our visual explanations method.
From a given classifier, we train two generators to produce from an input image
the so called similar and adversarial images. The similar image shall be
classified as the input image whereas the adversarial shall not. Visual
explanation is built as the difference between these two generated images.
Using metrics from the literature, our method outperforms state-of-the-art
approaches. The proposed approach is model-agnostic and has a low computation
burden at prediction time. Thus, it is adapted for real-time systems. Finally,
we show that random geometric augmentations applied to the original image play
a regularization role that improves several previously proposed explanation
methods. We validate our approach on a large chest X-ray database.
