Journal article
Augmenting the National Institutes of Health Chest Radiograph Dataset with Expert Annotations of Possible Pneumonia
Radiology. Artificial intelligence, Vol.1(1), pp.e180041-e180041
01/01/2019
DOI: 10.1148/ryai.2019180041
PMCID: PMC8017407
PMID: 33937785
Abstract
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.
Details
- Title: Subtitle
- Augmenting the National Institutes of Health Chest Radiograph Dataset with Expert Annotations of Possible Pneumonia
- Creators
- George Shih - Cornell UniversityCarol C. Wu - Image Guided Cancer Therapy Research ProgramSafwan S. Halabi - Stanford UniversityMarc D. Kohli - University of California, San FranciscoLuciano M. Prevedello - The Ohio State UniversityTessa S. Cook - University of PennsylvaniaArjun Sharma - Amita HealthJudith K. Amorosa - Rutgers, The State University of New JerseyVeronica Arteaga - University of ArizonaMaya Galperin-Aizenberg - University of PennsylvaniaRitu R. Gill - Harvard University ,Myrna C. B. Godoy - Image Guided Cancer Therapy Research ProgramStephen Hobbs - University of KentuckyJean Jeudy - University of Maryland, BaltimoreArchana Laroia - University of IowaPalmi N. Shah - Rush UniversityDharshan Vummidi - University of Michigan–Ann ArborKavitha Yaddanapudi - Stony Brook UniversityAnouk Stein - MD.ai
- Resource Type
- Journal article
- Publication Details
- Radiology. Artificial intelligence, Vol.1(1), pp.e180041-e180041
- Publisher
- RADIOLOGICAL SOC NORTH AMERICA (RSNA)
- DOI
- 10.1148/ryai.2019180041
- PMID
- 33937785
- PMCID
- PMC8017407
- ISSN
- 2638-6100
- eISSN
- 2638-6100
- Number of pages
- 5
- Grant note
- Beryl Institute ACRIN ACR Innovation grant
- Language
- English
- Date published
- 01/01/2019
- Academic Unit
- Radiology; Internal Medicine
- Record Identifier
- 9984318695802771
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