Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Elshaimaa Sharaf"'
Autor:
Romane Gauriau, Bernardo C. Bizzo, Donnella S. Comeau, James M. Hillis, Christopher P. Bridge, John K. Chin, Jayashri Pawar, Ali Pourvaziri, Ivana Sesic, Elshaimaa Sharaf, Jinjin Cao, Flavia T. C. Noro, Walter F. Wiggins, M. Travis Caton, Felipe Kitamura, Keith J. Dreyer, John F. Kalafut, Katherine P. Andriole, Stuart R. Pomerantz, Ramon G. Gonzalez, Michael H. Lev
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
Abstract Non-contrast head CT (NCCT) is extremely insensitive for early ( 0.98). When this 150 CT test set was expanded to include a total of 364 CT scans with a more heterogeneous distribution of infarct locations (94 stroke-negative, 270 stroke-pos
Externí odkaz:
https://doaj.org/article/854bbf674fbe487dae58da0a4762c5f1
Autor:
Christopher P. Bridge, Bernardo C. Bizzo, James M. Hillis, John K. Chin, Donnella S. Comeau, Romane Gauriau, Fabiola Macruz, Jayashri Pawar, Flavia T. C. Noro, Elshaimaa Sharaf, Marcelo Straus Takahashi, Bradley Wright, John F. Kalafut, Katherine P. Andriole, Stuart R. Pomerantz, Stefano Pedemonte, R. Gilberto González
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-11 (2022)
Abstract Stroke is a leading cause of death and disability. The ability to quickly identify the presence of acute infarct and quantify the volume on magnetic resonance imaging (MRI) has important treatment implications. We developed a machine learnin
Externí odkaz:
https://doaj.org/article/157c664d20a9403091eb001175b7373f
Autor:
Romane Gauriau, John K Chin, Christopher P. Bridge, James Hillis, Bradley Wright, Elshaimaa Sharaf, Jayashri Pawar, R. Gilberto Gonzalez, Stefano Pedemonte, John Francis Kalafut, Bernardo Bizzo, Stuart R. Pomerantz, Katherine P. Andriole, Fabiola B. C. Macruz, Marcelo Straus Takahashi, Donnella S Comeau, Flavia T C Noro
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-11 (2022)
BackgroundStroke is a leading cause of death and disability. The ability to quickly identify the presence of acute infarct and quantify the volume on magnetic resonance imaging (MRI) has important treatment implications. MethodsWe developed a machine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::03e4e048dcd8c86260633466ae7a316c
https://doaj.org/article/157c664d20a9403091eb001175b7373f
https://doaj.org/article/157c664d20a9403091eb001175b7373f
Autor:
Romane Gauriau, Bernardo C. Bizzo, Donnella S. Comeau, James M. Hillis, Christopher P. Bridge, John K. Chin, Jayashri Pawar, Ali Pourvaziri, Ivana Sesic, Elshaimaa Sharaf, Jinjin Cao, Flavia T. C. Noro, Walter F. Wiggins, M. Travis Caton, Felipe Kitamura, Keith J. Dreyer, John F. Kalafut, Katherine P. Andriole, Stuart R. Pomerantz, Ramon G. Gonzalez, Michael H. Lev
Publikováno v:
Scientific reports. 13(1)
Non-contrast head CT (NCCT) is extremely insensitive for early (2 > 0.98). When this 150 CT test set was expanded to include a total of 364 CT scans with a more heterogeneous distribution of infarct locations (94 stroke-negative, 270 stroke-positive
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1885ccb00088b6a641888d115188ee75
https://pubmed.ncbi.nlm.nih.gov/36604467
https://pubmed.ncbi.nlm.nih.gov/36604467
Autor:
Vitor Lavor, Varun Buch, Behrooz Hashemian, Jayashree Kalpathy-Cramer, Nir Neumark, Daguang Xu, Prerna Dogra, Miao Zhang, Yan Cheng, Etta D. Pisano, B. Min Yun, Vikash Gupta, Jay B. Patel, Ahmed Harouni, Keith J. Dreyer, Alvin Ihsani, Bryan Chen, Praveer Singh, Richard D. White, Wenqi Li, Colin B. Compas, Sharut Gupta, Thomas J. Schultz, Meesam Shah, Jesse Tetreault, Daniel L. Rubin, Sean Ko, Ken Chang, Laura Coombs, Ram C. Naidu, Evan Leibovitz, Holger R. Roth, Liangqiong Qu, Yuhong Wen, Katharina Hoebel, Ittai Dayan, Bernardo Bizzo, Felipe Kitamura, Matheus Ribeiro Furtado de Mendonça, Mona Flores, Elshaimaa Sharaf, Selnur Erdal, Adam McCarthy
Publikováno v:
Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning ISBN: 9783030605476
DART/DCL@MICCAI
DART/DCL@MICCAI
Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting. Seve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a47ee65b646f784422a9598f7fbf893c
https://doi.org/10.1007/978-3-030-60548-3_18
https://doi.org/10.1007/978-3-030-60548-3_18
