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Synthetic Medical Images for Robust, Privacy-Preserving Training of Artificial Intelligence: Application to Retinopathy of Prematurity Diagnosis.

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第一作者： Aaron S,Coyner

第一单位： Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.

作者： Aaron S,Coyner ^[1] ; Jimmy S,Chen ^[2] ; Ken,Chang ^[3] ; Praveer,Singh ^[3] ; Susan,Ostmo ^[1] ; R V Paul,Chan ^[4] ; Michael F,Chiang ^[5] ; Jayashree,Kalpathy-Cramer ^[3] ; J Peter,Campbell ^[1] ; Imaging and Informatics in Retinopathy of Prematurity Consortium

作者单位： Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon. ^[1] Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.;Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, San Diego, California. ^[2] Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.;Center for Clinical Data Science, Massachusetts General Hospital and Boston Women's Hospital, Boston, Massachusetts. ^[3] Department of Ophthalmology and Visual Sciences, Eye and Ear Infirmary, University of Illinois, Chicago, Illinois. ^[4] National Eye Institute, National Institutes of Health, Bethesda, Maryland. ^[5]

关键词 AI, artificial intelligence Artificial intelligence CNN, convolutional neural network DL, deep learning Deep learning FID, Fréchet inception distance GAN, generative adversarial network Generative adversarial network PGAN, progressively growing generative adversarial network RFI, retinal fundus image ROP, retinopathy of prematurity RVM, retinal vessel map Retinopathy of prematurity UMAP, uniform manifold approximation and projection

DOI 10.1016/j.xops.2022.100126

PMID 36249693

发布时间 2024-09-05

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Synthetic Medical Images for Robust, Privacy-Preserving Training of Artificial Intelligence: Application to Retinopathy of Prematurity Diagnosis.

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