Computer Assisted Reading of Chest Radiographs

Abstract

Chest radiographs or X-ray images are a common diagnostic tool to identify different thoracic diseases and other abnormal cardiopulmonary conditions. The advancements of artificial intelligence paves the way to machine learning based computer-assisted systems that can support the radiologists in disease diagnosis and report generation from chest radio-graphs. In this work we report an implementation of a deep-learning based framework to interpret the disease signature from chest X-rays. The model was trained on a large dataset consisting of both frontal and lateral X-ray images of the chest with multiple thoracic disease labels. We report a mean area under ROC curve (AUC) of 0.86, with the AUC of individual diseases in the range of 0.76 to 0.93. We also generated disease-level colormaps to visually present the X-ray image region most indicative of the disease.