Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions

Fan, R., Alipour, K., Bowd, C., Christopher, M., Brye, N., Proudfoot, J. A., Goldbaum, M. H., Belghith, A., Girkin, C. A., Fazio, M. A., Liebmann, J. M., Weinreb, R. N., Pazzani, M., Kriegman, D., & Zangwill, L. M. (2023). Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions. Ophthalmology Science, 3(1), 100233. https://doi.org/10.1016/j.xops.2022.100233
Authors:
Rui Fan
Kamran Alipour
Christopher Bowd
Mark Christopher
Nicole Brye
James A. Proudfoot
Michael H. Goldbaum
Akram Belghith
Christopher A. Girkin
Massimo A. Fazio
Jeffrey M. Liebmann
Robert N. Weinreb
Michael J. Pazzani
David Kriegman
Linda M. Zangwill
Affiliated Authors:
Jeffrey M. Liebmann
Subjects:
Global Prevalence and Treatment of Glaucoma (OpenAlex Topic)
Detection and Management of Retinal Diseases (OpenAlex Topic)
Age-Related Macular Degeneration Research (OpenAlex Topic)
Author Keywords:
deep learning
fundus photographs
glaucoma detection
vision
vision transformers
ai, artificial intelligence
auroc, areas under the receiver operating characteristic curve
ci, confidence interval
cnn, convolutional neural network
dl, deep learning
deit, data-efficient image transformer
lag, large-scale attention-based glaucoma
ohts, ocular hypertension treatment study
poag, primary open-angle glaucoma
sota, state-of-the-art
vf, visual field
vit, vision transformer
elink
Publication Type:
Article
Unique ID:
10.1016/j.xops.2022.100233
PMID:
36545260
DOI:
10.1016/j.xops.2022.100233
Journal:
Ophthalmology Science
Publication Date:
Data Source:
OpenAlex
Source Link:
View OpenAlex Record
View PubMed Record
View Scopus Record
View Web of Science Record

Record Created: