Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions: Transformer for Improved Generalization

PURPOSE: To compare the diagnostic accuracy and explainability of a Vision Transformer deep learning technique, Data-efficient image Transformer (DeiT), and ResNet-50, trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS) to detect primary open-angle glaucoma (POAG) and i...

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Detalles Bibliográficos
Autores principales: Fan, Rui, Alipour, Kamran, Bowd, Christopher, Christopher, Mark, Brye, Nicole, Proudfoot, James A., Goldbaum, Michael H., Belghith, Akram, Girkin, Christopher A., Fazio, Massimo A., Liebmann, Jeffrey M., Weinreb, Robert N., Pazzani, Michael, Kriegman, David, Zangwill, Linda M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Artificial Intelligence and Big Data
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762193/
https://www.ncbi.nlm.nih.gov/pubmed/36545260
http://dx.doi.org/10.1016/j.xops.2022.100233

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762193/
https://www.ncbi.nlm.nih.gov/pubmed/36545260
http://dx.doi.org/10.1016/j.xops.2022.100233

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