Bounding Box Supervision Benefits Lung Pathology Classification in Pulmonary X-Rays

Abstract

Classification and object detection are computer vision tasks with successful, clinical applications in medical imaging. Yet, the increased effort required of expert readers in order to annotate bounding boxes on medical images has yet to be quantitatively justified in terms of added value to identifying pathologies. In this study, we show preliminary results on the classification and localization of 17 most common chest pathologies on a private dataset of 15,000 radiographs from two Romanian public hospitals. Next, we quantitatively compare the extra added value of the bounding box information at training time, concerning classification performance improvements. Two types of architectures were trained on publicly available and private data, classification architectures (i.e., InceptionNet V3), tasked with identifying pathologies in chest radiographies and object detection architectures (i.e., Faster R-CNN), tasked with localizing the regions of interest on the image. Both achieved high classification performance (i.e., 90.52 and 88.94 mean AUROC, respectively). The object detector, however, reached superior classification performance, thus proving the additional bounding box information available at training time, benefits patient-level pathology identification as well.