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The development of diagnostic models for detecting and diagnosing pediatricdiseases in CXR scans is undertaken due to the lack of high-qualityphysician-annotated datasets. To overcome this challenge, we introduce andrelease PediCXR, a new pediatric CXR dataset of 9,125 studies retrospectivelycollected from a major pediatric hospital in Vietnam between 2020 and 2021.Each scan was manually annotated by a pediatric radiologist with more than tenyears of experience. The dataset was labeled for the presence of 36 criticalfindings and 15 diseases. In particular, each abnormal finding was identifiedvia a rectangle bounding box on the image. To the best of our knowledge, thisis the first and largest pediatric CXR dataset containing lesion-levelannotations and image-level labels for the detection of multiple findings anddiseases. For algorithm development, the dataset was divided into a trainingset of 7,728 and a test set of 1,397. To encourage new advances in pediatricCXR interpretation using data-driven approaches, we provide a detaileddescription of the PediCXR data sample and make the dataset publicly availableon https://physionet.org/content/pedicxr/1.0.0/

PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children