  We propose a BlackBox Counterfactual Explainer, designed to explain image
classification models for medical applications. Classical approaches (e.g.,
saliency maps) that assess feature importance do not explain "how" imaging
features in important anatomical regions are relevant to the classification
decision. Our framework explains the decision for a target class by gradually
"exaggerating" the semantic effect of the class in a query image. We adopted a
Generative Adversarial Network (GAN) to generate a progressive set of
perturbations to a query image, such that the classification decision changes
from its original class to its negation. We used counterfactual explanations
from our framework to audit a classifier trained on a chest x-ray dataset with
multiple labels. We proposed clinically-relevant quantitative metrics such as
cardiothoracic ratio and the score of a healthy costophrenic recess to evaluate
our explanations.
  We conducted a human-grounded experiment with diagnostic radiology residents
to compare different styles of explanations (no explanation, saliency map,
cycleGAN explanation, and our counterfactual explanation) by evaluating
different aspects of explanations: (1) understandability, (2) classifier's
decision justification, (3) visual quality, (d) identity preservation, and (5)
overall helpfulness of an explanation to the users. Our results show that our
counterfactual explanation was the only explanation method that significantly
improved the users' understanding of the classifier's decision compared to the
no-explanation baseline. Our metrics established a benchmark for evaluating
model explanation methods in medical images. Our explanations revealed that the
classifier relied on clinically relevant radiographic features for its
diagnostic decisions, thus making its decision-making process more transparent
to the end-user.
