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Solving data quality issues of fundus images in real-world settings by ophthalmic AI
Liu et al.(1) develop a deep-learning-based flow cytometry-like image quality classifier, DeepFundus, for the automated, high-throughput, and multidimensional classification of fundus image quality. DeepFundus significantly improves the real-world performance of established artificial intelligence d...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9975325/ https://www.ncbi.nlm.nih.gov/pubmed/36812885 http://dx.doi.org/10.1016/j.xcrm.2023.100951 |
_version_ | 1784898851871653888 |
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author | Li, Zhongwen Chen, Wei |
author_facet | Li, Zhongwen Chen, Wei |
author_sort | Li, Zhongwen |
collection | PubMed |
description | Liu et al.(1) develop a deep-learning-based flow cytometry-like image quality classifier, DeepFundus, for the automated, high-throughput, and multidimensional classification of fundus image quality. DeepFundus significantly improves the real-world performance of established artificial intelligence diagnostics in detecting multiple retinopathies. |
format | Online Article Text |
id | pubmed-9975325 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-99753252023-03-02 Solving data quality issues of fundus images in real-world settings by ophthalmic AI Li, Zhongwen Chen, Wei Cell Rep Med Preview Liu et al.(1) develop a deep-learning-based flow cytometry-like image quality classifier, DeepFundus, for the automated, high-throughput, and multidimensional classification of fundus image quality. DeepFundus significantly improves the real-world performance of established artificial intelligence diagnostics in detecting multiple retinopathies. Elsevier 2023-02-21 /pmc/articles/PMC9975325/ /pubmed/36812885 http://dx.doi.org/10.1016/j.xcrm.2023.100951 Text en © 2023. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Preview Li, Zhongwen Chen, Wei Solving data quality issues of fundus images in real-world settings by ophthalmic AI |
title | Solving data quality issues of fundus images in real-world settings by ophthalmic AI |
title_full | Solving data quality issues of fundus images in real-world settings by ophthalmic AI |
title_fullStr | Solving data quality issues of fundus images in real-world settings by ophthalmic AI |
title_full_unstemmed | Solving data quality issues of fundus images in real-world settings by ophthalmic AI |
title_short | Solving data quality issues of fundus images in real-world settings by ophthalmic AI |
title_sort | solving data quality issues of fundus images in real-world settings by ophthalmic ai |
topic | Preview |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9975325/ https://www.ncbi.nlm.nih.gov/pubmed/36812885 http://dx.doi.org/10.1016/j.xcrm.2023.100951 |
work_keys_str_mv | AT lizhongwen solvingdataqualityissuesoffundusimagesinrealworldsettingsbyophthalmicai AT chenwei solvingdataqualityissuesoffundusimagesinrealworldsettingsbyophthalmicai |