Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions

• Published in: Ophthalmology science (Online) Vol. 3; no. 1; p. 100233
• Main Authors: 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.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.03.2023; Elsevier
• Subjects: Deep learning; Fundus photographs; Glaucoma detection; Vision Transformers; Fundus photographs; POAG; ViT; CNN; CI; AI; DL; AUROC; DeiT; SoTA; Deep learning; VF; LAG; Vision Transformers; OHTS; Glaucoma detection
• ISSN: 2666-9145; 2666-9145
• DOI: 10.1016/j.xops.2022.100233

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 identify t...