  The high complexity of deep learning models is associated with the difficulty
of explaining what evidence they recognize as correlating with specific disease
labels. This information is critical for building trust in models and finding
their biases. Until now, automated deep learning visualization solutions have
identified regions of images used by classifiers, but these solutions are too
coarse, too noisy, or have a limited representation of the way images can
change. We propose a novel method for formulating and presenting spatial
explanations of disease evidence, called deformation field interpretation with
generative adversarial networks (DeFI-GAN). An adversarially trained generator
produces deformation fields that modify images of diseased patients to resemble
images of healthy patients. We validate the method studying chronic obstructive
pulmonary disease (COPD) evidence in chest x-rays (CXRs) and Alzheimer's
disease (AD) evidence in brain MRIs. When extracting disease evidence in
longitudinal data, we show compelling results against a baseline producing
difference maps. DeFI-GAN also highlights disease biomarkers not found by
previous methods and potential biases that may help in investigations of the
dataset and of the adopted learning methods.
