  We propose a BlackBox \emph{Counterfactual Explainer} that is explicitly
developed for medical imaging applications. Classical approaches (e.g. saliency
maps) assessing feature importance do not explain \emph{how} and \emph{why}
variations in a particular anatomical region is relevant to the outcome, which
is crucial for transparent decision making in healthcare application. Our
framework explains the outcome by gradually \emph{exaggerating} the semantic
effect of the given outcome label. Given a query input to a classifier,
Generative Adversarial Networks produce a progressive set of perturbations to
the query image that gradually changes the posterior probability from its
original class to its negation. We design the loss function to ensure that
essential and potentially relevant details, such as support devices, are
preserved in the counterfactually generated images. We provide an extensive
evaluation of different classification tasks on the chest X-Ray images. Our
experiments show that a counterfactually generated visual explanation is
consistent with the disease's clinical relevant measurements, both
quantitatively and qualitatively.
