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Improved Training Efficiency for Retinopathy of Prematurity Deep Learning Models Using Comparison versus Class Labels.
Hanif, Adam; Yildiz, Ilkay; Tian, Peng; Kalkanli, Beyza; Erdogmus, Deniz; Ioannidis, Stratis; Dy, Jennifer; Kalpathy-Cramer, Jayashree; Ostmo, Susan; Jonas, Karyn; Chan, R V Paul; Chiang, Michael F; Campbell, J Peter.
Afiliação
  • Hanif A; Department of Ophthalmology, Oregon Health & Science University, Portland, Oregon.
  • Yildiz I; Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts.
  • Tian P; Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts.
  • Kalkanli B; Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts.
  • Erdogmus D; Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts.
  • Ioannidis S; Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts.
  • Dy J; Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts.
  • Kalpathy-Cramer J; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging Clinical Computational Neuroimaging Group, Charlestown, Massachusetts.
  • Ostmo S; Department of Ophthalmology, Oregon Health & Science University, Portland, Oregon.
  • Jonas K; Department of Ophthalmology, University of Illinois at Chicago College of Medicine, Chicago, Illinois.
  • Chan RVP; Department of Ophthalmology, University of Illinois at Chicago College of Medicine, Chicago, Illinois.
  • Chiang MF; National Eye Institute, National Institutes of Health, Bethesda, Maryland.
  • Campbell JP; Department of Ophthalmology, Oregon Health & Science University, Portland, Oregon.
Ophthalmol Sci ; 2(2): 100122, 2022 Jun.
Article em En | MEDLINE | ID: mdl-36249702

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Observational_studies / Prognostic_studies Idioma: En Revista: Ophthalmol Sci Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Observational_studies / Prognostic_studies Idioma: En Revista: Ophthalmol Sci Ano de publicação: 2022 Tipo de documento: Article