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Federated learning for predicting clinical outcomes in patients with COVID-19.
Dayan I, Roth HR, Zhong A, Harouni A, Gentili A, Abidin AZ, Liu A, Costa AB, Wood BJ, Tsai CS, Wang CH, Hsu CN, Lee CK, Ruan P, Xu D, Wu D, Huang E, Kitamura FC, Lacey G, de Antônio Corradi GC, Nino G, Shin HH, Obinata H, Ren H, Crane JC, Tetreault J, Guan J, Garrett JW, Kaggie JD, Park JG, Dreyer K, Juluru K, Kersten K, Rockenbach MABC, Linguraru MG, Haider MA, AbdelMaseeh M, Rieke N, Damasceno PF, E Silva PMC, Wang P, Xu S, Kawano S, Sriswasdi S, Park SY, Grist TM, Buch V, Jantarabenjakul W, Wang W, Tak WY, Li X, Lin X, Kwon YJ, Quraini A, Feng A, Priest AN, Turkbey B, Glicksberg B, Bizzo B, Kim BS, Tor-Díez C, Lee CC, Hsu CJ, Lin C, Lai CL, Hess CP, Compas C, Bhatia D, Oermann EK, Leibovitz E, Sasaki H, Mori H, Yang I, Sohn JH, Murthy KNK, Fu LC, de Mendonça MRF, Fralick M, Kang MK, Adil M, Gangai N, Vateekul P, Elnajjar P, Hickman S, Majumdar S, McLeod SL, Reed S, Gräf S, Harmon S, Kodama T, Puthanakit T, Mazzulli T, de Lavor VL, Rakvongthai Y, Lee YR, Wen Y, Gilbert FJ, Flores MG, Li Q. Dayan I, et al. Nat Med. 2021 Oct;27(10):1735-1743. doi: 10.1038/s41591-021-01506-3. Epub 2021 Sep 15. Nat Med. 2021. PMID: 34526699 Free PMC article.
Federated Learning used for predicting outcomes in SARS-COV-2 patients.
Flores M, Dayan I, Roth H, Zhong A, Harouni A, Gentili A, Abidin A, Liu A, Costa A, Wood B, Tsai CS, Wang CH, Hsu CN, Lee CK, Ruan C, Xu D, Wu D, Huang E, Kitamura F, Lacey G, César de Antônio Corradi G, Shin HH, Obinata H, Ren H, Crane J, Tetreault J, Guan J, Garrett J, Park JG, Dreyer K, Juluru K, Kersten K, Bezerra Cavalcanti Rockenbach MA, Linguraru M, Haider M, AbdelMaseeh M, Rieke N, Damasceno P, Cruz E Silva PM, Wang P, Xu S, Kawano S, Sriswasdi S, Park SY, Grist T, Buch V, Jantarabenjakul W, Wang W, Tak WY, Li X, Lin X, Kwon F, Gilbert F, Kaggie J, Li Q, Quraini A, Feng A, Priest A, Turkbey B, Glicksberg B, Bizzo B, Kim BS, Tor-Diez C, Lee CC, Hsu CJ, Lin C, Lai CL, Hess C, Compas C, Bhatia D, Oermann E, Leibovitz E, Sasaki H, Mori H, Yang I, Sohn JH, Keshava Murthy KN, Fu LC, Furtado de Mendonça MR, Fralick M, Kang MK, Adil M, Gangai N, Vateekul P, Elnajjar P, Hickman S, Majumdar S, McLeod S, Reed S, Graf S, Harmon S, Kodama T, Puthanakit T, Mazzulli T, de Lima Lavor V, Rakvongthai Y, Lee YR, Wen Y. Flores M, et al. Among authors: dayan i. Res Sq. 2021 Jan 8:rs.3.rs-126892. doi: 10.21203/rs.3.rs-126892/v1. Preprint. Res Sq. 2021. PMID: 33442676 Free PMC article. Updated.
Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19.
Li MD, Arun NT, Aggarwal M, Gupta S, Singh P, Little BP, Mendoza DP, Corradi GCA, Takahashi MS, Ferraciolli SF, Succi MD, Lang M, Bizzo BC, Dayan I, Kitamura FC, Kalpathy-Cramer J. Li MD, et al. Among authors: dayan i. Medicine (Baltimore). 2022 Jul 22;101(29):e29587. doi: 10.1097/MD.0000000000029587. Medicine (Baltimore). 2022. PMID: 35866818 Free PMC article.
Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19.
Zhong A, Li X, Wu D, Ren H, Kim K, Kim Y, Buch V, Neumark N, Bizzo B, Tak WY, Park SY, Lee YR, Kang MK, Park JG, Kim BS, Chung WJ, Guo N, Dayan I, Kalra MK, Li Q. Zhong A, et al. Among authors: dayan i. Med Image Anal. 2021 May;70:101993. doi: 10.1016/j.media.2021.101993. Epub 2021 Feb 7. Med Image Anal. 2021. PMID: 33711739 Free PMC article.
Hierarchical axion inflation.
Ben-Dayan I, Pedro FG, Westphal A. Ben-Dayan I, et al. Phys Rev Lett. 2014 Dec 31;113(26):261301. doi: 10.1103/PhysRevLett.113.261301. Epub 2014 Dec 23. Phys Rev Lett. 2014. PMID: 25615300
35 results