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Detection of Pneumothorax with Deep Learning Models: Learning From Radiologist Labels vs Natural Language Processing Model Generated Labels.
Hallinan, James Thomas Patrick Decourcy; Feng, Mengling; Ng, Dianwen; Sia, Soon Yiew; Tiong, Vincent Tze Yang; Jagmohan, Pooja; Makmur, Andrew; Thian, Yee Liang.
Affiliation
  • Hallinan JTPD; Department of Diagnostic Imaging, National University Hospital, Singapore. Electronic address: james_hallinan@nuhs.edu.sg.
  • Feng M; Saw Swee Hock School of Public Health, Institute of Data Science, Yong Loo Lin School of Medicine, National University Health System, National University of Singapore, Singapore.
  • Ng D; Department of Diagnostic Imaging, National University Hospital, Singapore; Saw Swee Hock School of Public Health, Institute of Data Science, Yong Loo Lin School of Medicine, National University Health System, National University of Singapore, Singapore.
  • Sia SY; Department of Diagnostic Imaging, National University Hospital, Singapore.
  • Tiong VTY; Department of Diagnostic Imaging, National University Hospital, Singapore.
  • Jagmohan P; Department of Diagnostic Imaging, National University Hospital, Singapore.
  • Makmur A; Department of Diagnostic Imaging, National University Hospital, Singapore.
  • Thian YL; Department of Diagnostic Imaging, National University Hospital, Singapore.
Acad Radiol ; 29(9): 1350-1358, 2022 09.
Article de En | MEDLINE | ID: mdl-34649780
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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Pneumothorax / Apprentissage profond Type d'étude: Diagnostic_studies / Observational_studies / Prognostic_studies Limites: Humans Langue: En Journal: Acad Radiol Sujet du journal: RADIOLOGIA Année: 2022 Type de document: Article Pays de publication: États-Unis d'Amérique
Texte intégral
Ajouter à My VHL
Imprimer
XML
PubMed Links
Recherche sur Google

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Pneumothorax / Apprentissage profond Type d'étude: Diagnostic_studies / Observational_studies / Prognostic_studies Limites: Humans Langue: En Journal: Acad Radiol Sujet du journal: RADIOLOGIA Année: 2022 Type de document: Article Pays de publication: États-Unis d'Amérique