Automatic Bounding Box Annotation of Chest X-Ray Data for Localization of Abnormalities

Automatic detection of findings and their locations in chest x-ray studies is an important research area for AI application in healthcare. Whereas for finding classification tasks image-level labeling suffices, additional annotation in the form of bounding boxes is required for detection of findings locations. However, the process of locally marking findings on chest xray images is both time consuming and costly as it needs to be performed by radiologists. To address this problem, weakly supervised approaches have been employed to depict finding locations by looking at attention maps produced by convolution networks trained for findings classification. However, these approaches have not shown much promise so far and raised concerns whether the networks are actually focusing on the right abnormality regions. With this in mind, in this paper we propose an automatic approach for labeling chest x-ray images for …