  Most of the existing chest X-ray datasets include labels from a list of
findings 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
(https://www.physionet.org/content/vindr-cxr/1.0.0/) in DICOM format along with
the labels of both the training set and the test set.
