Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysis
It addresses the need for explainable AI in medical image analysis, enabling transparent labeling and interpretation for healthcare professionals, though it appears incremental by building on existing cycleGAN and report generation methods.
The paper tackles the problem of generating trustworthy explanations for medical image diagnosis by proposing a cyclic-GAN framework that produces congruent image-report pairs, achieving state-of-the-art performance in some cases on the Indiana Chest X-ray dataset.
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