  Despite the surge of deep learning in the past decade, some users are
skeptical to deploy these models in practice due to their black-box nature.
Specifically, in the medical space where there are severe potential
repercussions, we need to develop methods to gain confidence in the models'
decisions. To this end, we propose a novel medical imaging generative
adversarial framework, medXGAN (medical eXplanation GAN), to visually explain
what a medical classifier focuses on in its binary predictions. By encoding
domain knowledge of medical images, we are able to disentangle anatomical
structure and pathology, leading to fine-grained visualization through latent
interpolation. Furthermore, we optimize the latent space such that
interpolation explains how the features contribute to the classifier's output.
Our method outperforms baselines such as Gradient-Weighted Class Activation
Mapping (Grad-CAM) and Integrated Gradients in localization and explanatory
ability. Additionally, a combination of the medXGAN with Integrated Gradients
can yield explanations more robust to noise. The code is available at:
https://avdravid.github.io/medXGAN_page/.
