@article{c24b4d6d3af749948d9fedb94e73a360,
title = "ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI",
abstract = "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).",
keywords = "Benchmarking, Datasets, Deep learning, MRI, Machine learning, Prediction models, Stroke, Stroke outcome",
author = "Stefan Winzeck and Arsany Hakim and Richard McKinley and Pinto, {Jos{\'e} A.A.D.S.R.} and Victor Alves and Carlos Silva and Maxim Pisov and Egor Krivov and Mikhail Belyaev and Miguel Monteiro and Arlindo Oliveira and Youngwon Choi and Paik, {Myunghee Cho} and Yongchan Kwon and Hanbyul Lee and Kim, {Beom Joon} and Won, {Joong Ho} and Mobarakol Islam and Hongliang Ren and David Robben and Paul Suetens and Enhao Gong and Yilin Niu and Junshen Xu and Pauly, {John M.} and Christian Lucas and Heinrich, {Mattias P.} and Rivera, {Luis C.} and Castillo, {Laura S.} and Daza, {Laura A.} and Beers, {Andrew L.} and Pablo Arbelaezs and Oskar Maier and Ken Chang and Brown, {James M.} and Jayashree Kalpathy-Cramer and Greg Zaharchuk and Roland Wiest and Mauricio Reyes",
note = "Funding Information: This work was supported by the Graduate School for Computing in Medicine and Life Sciences funded by Germany{\textquoteright}s Excellence Initiative [DFG GSC 235/2]. We would also like to thank Nvidia Corporation for their support by providing us with a Titan Xp graphics card. Funding Information: Fundacao para a Ciencia e Tecnologia (FCT), Portugal (scholarship number PD/BD/113968/2015). FCT with the UID/EEA/04436/2013, by FEDER funds through COMPETE 2020, POCI-01-0145-FEDER-006941. NIH Blueprint for Neuroscience Research (T90DA022759/R90DA023427) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health under award number 5T32EB1680. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. PAC-PRECISE-LISBOA-01-0145-FEDER-016394. FEDER-POR Lisboa 2020-Programa Operacional Regional de Lisboa PORTUGAL 2020 and Funda{\c c}{\~a}o para a Ci{\^e}ncia e a Tecnologia. GPU computing resources provided by the MGH and BWH Center for Clinical Data Science Graduate School for Computing in Medicine and Life Sciences funded by Germany{\textquoteright}s Excellence Initiative [DFG GSC 235/2]. National Research National Research Foundation of Korea (NRF) MSIT, NRF-2016R1C1B1012002, MSIT, No. 2014R1A4A1007895, NRF-2017R1A2B4008956 Swiss National Science Foundation-DACH 320030L_163363. Funding Information: Adriano Pinto was supported by a scholarship from the Funda{\c c}{\~a}o para a Ci{\^e}ncia e Tecnologia (FCT), Portugal (scholarship number PD/BD/113968/2015). This work is supported by FCT with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionaliza{\c c}{\~a}o (POCI) with the reference project POCI-01-0145-FEDER-006941. We acknowledge support from the Swiss National Science Foundation - DACH 320030L 163363. Funding Information: This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451). Funding Information: This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT, NRF-2016R1C1B1012002). Joong-Ho Won{\textquoteright}s research was partially supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT, No. 2014R1A4A1007895). Myunghee Cho Paik{\textquoteright}s research was supported by the National Research Foundation of Korea under grant NRF-2017R1A2B4008956. Funding Information: This work was supported by PAC - PRECISE - LISBOA-01-0145-FEDER-016394, co-funded by FEDER through POR Lisboa 2020 -Programa Operacional Regional de Lisboa PORTUGAL 2020 and Funda{\c c}{\~a}o para a Ci{\^e}ncia e a Tecnologia. Funding Information: The authors acknowledge the support of the Herzstiftung. Publisher Copyright: {\textcopyright} 2007-2018 Frontiers Media S.A. All Rights Reserved.",
year = "2018",
month = sep,
day = "13",
doi = "10.3389/fneur.2018.00679",
language = "English (US)",
volume = "9",
journal = "Frontiers in Neurology",
issn = "1664-2295",
publisher = "Frontiers Research Foundation",
number = "SEP",
}