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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. Among authors: murthy knk. 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. Res Sq [Preprint]. 2021 Jan 8:rs.3.rs-126892. doi: 10.21203/rs.3.rs-126892/v1. Res Sq. 2021. Update in: Nat Med. 2021 Oct;27(10):1735-1743. doi: 10.1038/s41591-021-01506-3 PMID: 33442676 Free PMC article. Updated. Preprint.
Fast, light, and scalable: harnessing data-mined line annotations for automated tumor segmentation on brain MRI.
Swinburne NC, Yadav V, Murthy KNK, Elnajjar P, Shih HH, Panyam PK, Santilli A, Gutman DC, Pike L, Moss NS, Stone J, Hatzoglou V, Shah A, Juluru K, Shah SP, Holodny AI, Young RJ; M.S.K. MIND Consortium. Swinburne NC, et al. Among authors: murthy knk. Eur Radiol. 2023 Sep;33(9):6582-6591. doi: 10.1007/s00330-023-09583-3. Epub 2023 Apr 12. Eur Radiol. 2023. PMID: 37042979 Free PMC article.