摘要：Most of the existing chest X-ray datasets include labels from a list of fndings without specifying their locations on the radiographs . This limits the development of machine learning algorithms for the detection and localization of chest abnormalities . In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam . Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases . The released dataset is divided into a training set of 15,000 and a test set of 3,000 . Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists . We designed and built a labeling platform for DICOM images to facilitate these annotation procedures . All images are made publicly available in DICOM format along with the labels of both the training set and the test set .
