Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases-Reference-Cited by-同舟云学术

Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases

Published:2020-10-01 Issue:10 Volume:138 Page:1070
ISSN:2168-6165
Container-title:JAMA Ophthalmology
language:en
Short-container-title:JAMA Ophthalmol

Author:

Burlina Philippe¹²³,Paul William¹,Mathew Philip¹,Joshi Neil¹,Pacheco Katia D.⁴,Bressler Neil M.²⁵

Affiliation:

1. Applied Physics Laboratory, The Johns Hopkins University, Baltimore, Maryland

2. Wilmer Eye Institute, Retina Division, The Johns Hopkins University School of Medicine, Baltimore, Maryland

3. Malone Center for Engineering in Healthcare, Department of Computer Science, The Johns Hopkins University, Baltimore, Maryland

4. Retina Division, Department of Ophthalmology, Brazilian Center of Vision Eye Hospital, Brasilia, Federal District, Brazil

5. Editor, JAMA Ophthalmology

Publisher

American Medical Association (AMA)

Subject

Ophthalmology

Link

https://jamanetwork.com/journals/jamaophthalmology/articlepdf/2770184/jamaophthalmology_burlina_2020_oi_200065_1601757572.58663.pdf

Reference21 articles.

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4. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks.;Burlina;JAMA Ophthalmol,2017

5. A randomized, placebo-controlled, clinical trial of high-dose supplementation with vitamins C and E, beta carotene, and zinc for age-related macular degeneration and vision loss: AREDS report no. 8.;Age-Related Eye Disease Study Research Group;Arch Ophthalmol,2001

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