학술논문
Federated learning for multi-center collaboration in ophthalmology: implications for clinical diagnosis and disease epidemiology
Document Type
Article
Author
Hanif, Adam; Lu, Charles; Chang, Ken; Singh, Praveer; Coyner, Aaron S.; Brown, James M.; Ostmo, Susan; Chan, RV Paul; Rubin, Daniel; Chiang, Michael F.; Kalpathy-Cramer, Jayashree; Campbell, J Peter; Chiang, Michael F.; Ostmo, Susan; Kim, Sang Jin; Sonmez, Kemal; Campbell, J. Peter; Schelonka, Robert; Coyner, Aaron; Chan, RV Paul; Jonas, Karyn; Kolli, Bhavana; Horowitz, Jason; Coki, Osode; Eccles, Cheryl-Ann; Sarna, Leora; Orlin, Anton; Berrocal, Audina; Negron, Catherin; Denser, Kimberly; Cumming, Kristi; Osentoski, Tammy; Check, Tammy; Zajechowski, Mary; Lee, Thomas; Nagiel, Aaron; Kruger, Evan; McGovern, Kathryn; Contractor, Dilshad; Havunjian, Margaret; Simmons, Charles; Murthy, Raghu; Galvis, Sharon; NNP; Rotter, Jerome; Chen, Ida; Li, Xiaohui; Taylor, Kent; Roll, Kaye; Hartnett, Mary Elizabeth; Owen, Leah; Moshfeghi, Darius; Nunez, Mariana; Wennber-Smith, Zac; Kalpathy-Cramer, Jayashree; Erdogmus, Deniz; Ioannidis, Stratis; Martinez-Castellanos, Maria Ana; Salinas-Longoria, Samantha; Romero, Rafael; Arriola, Andrea; Olguin-Manriquez, Francisco; Meraz-Gutierrez, Miroslava; Dulanto-Reinoso, Carlos M.; Montero-Mendoza, Cristina
Source
Ophthalmology Retina; 20220101, Issue: Preprints
Subject
Language
ISSN
24687219; 24686530
Abstract
To utilize a deep learning (DL) model trained via federated learning (FL), a method of collaborative training without sharing patient data, to delineate institutional differences in clinician diagnostic paradigms and disease epidemiology in retinopathy of prematurity (ROP).