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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...
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. |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Association for Research in Vision and Ophthalmology
2020
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Materias: | |
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 |
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