Addressing Artificial Intelligence Bias in Retinal Diagnostics

PURPOSE: This study evaluated generative methods to potentially mitigate artificial intelligence (AI) bias when diagnosing diabetic retinopathy (DR) resulting from training data imbalance or domain generalization, which occurs when deep learning systems (DLSs) face concepts at test/inference time th...

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Detalles Bibliográficos
Autores principales: Burlina, Philippe, Joshi, Neil, Paul, William, Pacheco, Katia D., Bressler, Neil M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7884292/
https://www.ncbi.nlm.nih.gov/pubmed/34003898
http://dx.doi.org/10.1167/tvst.10.2.13

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7884292/
https://www.ncbi.nlm.nih.gov/pubmed/34003898
http://dx.doi.org/10.1167/tvst.10.2.13

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