Effects of Study Population, Labeling and Training on Glaucoma Detection Using Deep Learning Algorithms

PURPOSE: To compare performance of independently developed deep learning algorithms for detecting glaucoma from fundus photographs and to evaluate strategies for incorporating new data into models. METHODS: Two fundus photograph datasets from the Diagnostic Innovations in Glaucoma Study/African Desc...

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Detalles Bibliográficos
Autores principales: Christopher, Mark, Nakahara, Kenichi, Bowd, Christopher, Proudfoot, James A., Belghith, Akram, Goldbaum, Michael H., Rezapour, Jasmin, Weinreb, Robert N., Fazio, Massimo A., Girkin, Christopher A., Liebmann, Jeffrey M., De Moraes, Gustavo, Murata, Hiroshi, Tokumo, Kana, Shibata, Naoto, Fujino, Yuri, Matsuura, Masato, Kiuchi, Yoshiaki, Tanito, Masaki, Asaoka, Ryo, Zangwill, Linda M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2020
Materias:
Special Issue
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7396194/
https://www.ncbi.nlm.nih.gov/pubmed/32818088
http://dx.doi.org/10.1167/tvst.9.2.27

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7396194/
https://www.ncbi.nlm.nih.gov/pubmed/32818088
http://dx.doi.org/10.1167/tvst.9.2.27

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