Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration-Reference-Cited by-同舟云学术

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Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration

Published:2019-03-01 Issue:3 Volume:137 Page:258
ISSN:2168-6165
Container-title:JAMA Ophthalmology
language:en
Short-container-title:JAMA Ophthalmol

Author:

Burlina Philippe M.¹²³,Joshi Neil¹,Pacheco Katia D.⁴,Liu T. Y. Alvin³,Bressler Neil M.³⁵

Affiliation:

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

2. Malone Center for Engineering in Healthcare, Baltimore, Maryland

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

4. Brasilian Center of Vision Eye Hospital, Brasilia, Distrito Federal, Brazil

5. Editor, JAMA Ophthalmology

Publisher

American Medical Association (AMA)

Subject

Ophthalmology

Link

http:///jamaophthalmology/article-pdf/137/3/258/17326524/jamaophthalmology_burlina_2019_oi_180105.pdf

Reference32 articles.

1. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.;Gulshan;JAMA,2016

2. Deep image mining for diabetic retinopathy screening.;Quellec;Med Image Anal,2017

3. Automated identification of diabetic retinopathy using deep learning.;Gargeya;Ophthalmology,2017

4. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes.;Ting;JAMA,2017

5. Clinically applicable deep learning for diagnosis and referral in retinal disease.;De Fauw;Nat Med,2018

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