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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Publicado no:Transl Vis Sci Technol
Main Authors: 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: Artigo
Idioma:Inglês
Publicado em: The Association for Research in Vision and Ophthalmology 2020
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Special Issue
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC7396194/
https://ncbi.nlm.nih.gov/pubmed/32818088
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1167/tvst.9.2.27
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