  Deep learning models used in medical image analysis are prone to raising
reliability concerns due to their black-box nature. To shed light on these
black-box models, previous works predominantly focus on identifying the
contribution of input features to the diagnosis, i.e., feature attribution. In
this work, we explore counterfactual explanations to identify what patterns the
models rely on for diagnosis. Specifically, we investigate the effect of
changing features within chest X-rays on the classifier's output to understand
its decision mechanism. We leverage a StyleGAN-based approach (StyleEx) to
create counterfactual explanations for chest X-rays by manipulating specific
latent directions in their latent space. In addition, we propose EigenFind to
significantly reduce the computation time of generated explanations. We
clinically evaluate the relevancy of our counterfactual explanations with the
help of radiologists. Our code is publicly available.
