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...

Descrizione completa

Salvato in:
Dettagli Bibliografici
Pubblicato in:Transl Vis Sci Technol
Autori principali: 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.
Natura: Artigo
Lingua:Inglês
Pubblicazione: The Association for Research in Vision and Ophthalmology 2020
Soggetti:
Special Issue
Accesso online: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
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne! !

Documenti analoghi