  We present a novel framework for explainable labeling and interpretation of
medical images. Medical images require specialized professionals for
interpretation, and are explained (typically) via elaborate textual reports.
Different from prior methods that focus on medical report generation from
images or vice-versa, we novelly generate congruent image--report pairs
employing a cyclic-Generative Adversarial Network (cycleGAN); thereby, the
generated report will adequately explain a medical image, while a
report-generated image that effectively characterizes the text visually should
(sufficiently) resemble the original. The aim of the work is to generate
trustworthy and faithful explanations for the outputs of a model diagnosing
chest x-ray images by pointing a human user to similar cases in support of a
diagnostic decision. Apart from enabling transparent medical image labeling and
interpretation, we achieve report and image-based labeling comparable to prior
methods, including state-of-the-art performance in some cases as evidenced by
experiments on the Indiana Chest X-ray dataset
