Publication:
The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs.

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dc.contributor.authorLakhani, Paras
dc.contributor.authorMongan, J
dc.contributor.authorSinghal, C
dc.contributor.authorZhou, Q
dc.contributor.authorAndriole, K P
dc.contributor.authorAuffermann, W F
dc.contributor.authorPrasanna, P M
dc.contributor.authorPham, T X
dc.contributor.authorPeterson, Michael
dc.contributor.authorBergquist, P J
dc.contributor.authorCook, T S
dc.contributor.authorFerraciolli, S F
dc.contributor.authorCorradi, G C A
dc.contributor.authorTakahashi, M S
dc.contributor.authorWorkman, C S
dc.contributor.authorParekh, M
dc.contributor.authorKamel, S I
dc.contributor.authorGalant, J
dc.contributor.authorMas-Sanchez, A
dc.contributor.authorBenítez, E C
dc.contributor.authorSánchez-Valverde, M
dc.contributor.authorJaques, L
dc.contributor.authorPanadero, M
dc.contributor.authorVidal, M
dc.contributor.authorCuliañez-Casas, M
dc.contributor.authorAngulo-Gonzalez, D
dc.contributor.authorLanger, S G
dc.contributor.authorde la Iglesia-Vayá, María
dc.contributor.authorShih, G
dc.date.accessioned2023-05-03T13:36:45Z
dc.date.available2023-05-03T13:36:45Z
dc.date.issued2022-09-28
dc.description.abstractWe describe the curation, annotation methodology, and characteristics of the dataset used in an artificial intelligence challenge for detection and localization of COVID-19 on chest radiographs. The chest radiographs were annotated by an international group of radiologists into four mutually exclusive categories, including "typical," "indeterminate," and "atypical appearance" for COVID-19, or "negative for pneumonia," adapted from previously published guidelines, and bounding boxes were placed on airspace opacities. This dataset and respective annotations are available to researchers for academic and noncommercial use.
dc.identifier.doi10.1007/s10278-022-00706-8
dc.identifier.essn1618-727X
dc.identifier.pmcPMC9518934
dc.identifier.pmid36171520
dc.identifier.pubmedURLhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9518934/pdf
dc.identifier.unpaywallURLhttps://link.springer.com/content/pdf/10.1007/s10278-022-00706-8.pdf
dc.identifier.urihttp://hdl.handle.net/10668/20430
dc.issue.number1
dc.journal.titleJournal of digital imaging
dc.journal.titleabbreviationJ Digit Imaging
dc.language.isoen
dc.organizationHospital Universitario Virgen del Rocío
dc.page.number365-372
dc.pubmedtypeJournal Article
dc.pubmedtypeResearch Support, Non-U.S. Gov't
dc.pubmedtypeResearch Support, U.S. Gov't, Non-P.H.S.
dc.rights.accessRightsopen access
dc.subjectArtificial Intelligence
dc.subjectCOVID-19
dc.subjectMachine Learning
dc.subjectPneumonia
dc.subjectRadiography
dc.subjectThorax
dc.subject.meshHumans
dc.subject.meshCOVID-19
dc.subject.meshArtificial Intelligence
dc.subject.meshRadiography
dc.subject.meshMachine Learning
dc.subject.meshRadiologists
dc.subject.meshRadiography, Thoracic
dc.titleThe 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs.
dc.typeresearch article
dc.type.hasVersionVoR
dc.volume.number36
dspace.entity.typePublication

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