Deep Learning for Glaucoma Detection and Identification of Novel Diagnostic Areas in Diverse Real-World Datasets-Reference-Cited by-同舟云学术

Deep Learning for Glaucoma Detection and Identification of Novel Diagnostic Areas in Diverse Real-World Datasets

Published:2022-05-12 Issue:5 Volume:11 Page:11
ISSN:2164-2591
Container-title:Translational Vision Science & Technology
language:en
Short-container-title:Trans. Vis. Sci. Tech.

Author:

Noury Erfan¹²,Mannil Suria S.³,Chang Robert T.³,Ran An Ran⁴,Cheung Carol Y.⁴,Thapa Suman S.⁵,Rao Harsha L.⁶,Dasari Srilakshmi⁶,Riyazuddin Mohammed⁶,Chang Dolly³,Nagaraj Sriharsha⁶,Tham Clement C.⁴,Zadeh Reza¹⁷

Affiliation:

1. Matroid, Palo Alto, CA, USA

2. University of Maryland at Baltimore County, Baltimore, MD, USA

3. Byers Eye Institute, Stanford University, Palo Alto, CA, USA

4. Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong

5. Tilganga Institute of Ophthalmology, Kathmandu, Nepal

6. Narayana Nethralaya, Bangalore, India

7. Stanford University, Stanford, CA, USA

Publisher

Association for Research in Vision and Ophthalmology (ARVO)

Subject

Ophthalmology,Biomedical Engineering

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5. In vivo evaluation of focal lamina cribrosa defects in glaucoma;Kiumehr;Arch Ophthalmol,2012

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