Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/65827

TítuloISLES 2016 and 2017-Benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI
Autor(es)Winzeck, Stefan
Hakim, Arsany
McKinley, Richard
Pinto, José A. A. D. S. R.
Alves, Victor
Silva, Carlos A.
Pisov, Maxim
Krivov, Egor
Belyaev, Mikhail
Monteiro, Miguel
Oliveira, Arlindo
Choi, Youngwon
Paik, Myunghee Cho
Kwon, Yongchan
Lee, Hanbyul
Kim, Beom Joon
Won, Joong-Ho
Islam, Mobarakol
Ren, Hongliang
Robben, David
Suetens, Paul
Gong, Enhao
Niu, Yilin
Xu, Junshen
Pauly, John M.
Lucas, Christian
Heinrich, Mattias P.
Rivera, Luis C.
Castillo, Laura S.
Daza, Laura A.
Beers, Andrew L.
Arbelaezs, Pablo
Maier, Oskar
Chang, Ken
Brown, James M.
Kalpathy-Cramer, Jayashree
Zaharchuk, Greg
Wiest, Roland
Reyes, Mauricio
Palavras-chavestroke
stroke outcome
machine learning
deep learning
benchmarking
datasets
MRI
prediction models
Data2018
EditoraFrontiers Media
RevistaFrontiers in Neurology
Resumo(s)Performance of models highly depend not only on the used algorithm but also the data set it was applied to. This makes the comparison of newly developed tools to previously published approaches difficult. Either researchers need to implement others' algorithms first, to establish an adequate benchmark on their data, or a direct comparison of new and old techniques is infeasible. The Ischemic Stroke Lesion Segmentation (ISLES) challenge, which has ran now consecutively for 3 years, aims to address this problem of comparability. ISLES 2016 and 2017 focused on lesion outcome prediction after ischemic stroke: By providing a uniformly pre-processed data set, researchers from all over the world could apply their algorithm directly. A total of nine teams participated in ISLES 2015, and 15 teams participated in ISLES 2016. Their performance was evaluated in a fair and transparent way to identify the state-of-the-art among all submissions. Top ranked teams almost always employed deep learning tools, which were predominately convolutional neural networks (CNNs). Despite the great efforts, lesion outcome prediction persists challenging. The annotated data set remains publicly available and new approaches can be compared directly via the online evaluation system, serving as a continuing benchmark (www.isles-challenge.org).
TipoArtigo
URIhttps://hdl.handle.net/1822/65827
DOI10.3389/fneur.2018.00679
ISSN1664-2295
Versão da editorahttps://www.frontiersin.org/articles/10.3389/fneur.2018.00679/full
Arbitragem científicayes
AcessoAcesso aberto
Aparece nas coleções:CMEMS - Artigos em revistas internacionais/Papers in international journals

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