Augmenting the National Institutes of Health Chest Radiograph Dataset with Expert Annotations of Possible Pneumonia.

Radiol Artif Intell

Department of Radiology, Weill Cornell Medical College, 525 E 68th St, Box 141, New York, NY 10065 (G.S.); Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (C.C.W., M.C.B.G.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (S.S.H.); Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, Calif (M.D.K.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C., M.G.); Department of Radiology, Amita Health, Chicago, Ill (A. Sharma); Department of Radiology, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ (J.K.A.); Department of Radiology, University of Arizona College of Medicine, Tucson, Ariz (V.A.); Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Mass (R.R.G.); Department of Radiology, University of Kentucky College of Medicine, Lexington, Ky (S.H.); Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (J.J.); Department of Radiology, University of Iowa Carver College of Medicine, Iowa City, Iowa (A.L.); Department of Diagnostic Radiology, Rush University Medical Center, Chicago, Ill (P.N.S.); Department of Radiology, University of Michigan Health System, Ann Arbor, Mich (D.V.); Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY (K.Y.); and MD.ai, New York, NY (A. Stein).

Published: January 2019

This dataset is intended to be used for machine learning and is composed of annotations with bounding boxes for pulmonary opacity on chest radiographs which may represent pneumonia in the appropriate clinical setting.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8017407PMC
http://dx.doi.org/10.1148/ryai.2019180041DOI Listing

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