@article{WinzeckFrontiers2018,
	abstract = {Performance of models highly depend not only on the used algorithm but also the data set it was
applied to. This makes newly suggested tools difficult to compare, and often requires researchers
to implement a previously published algorithm first to have an adequate benchmark on their
data set. The Ischemic Stroke Lesion Segmentation (ISLES) challenge, ran now consecutively
for three years, aims to address this problem of comparability. ISLES 2016 & 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 and 15 research teams participated for ISLES 2016 and ISLES 2017, respectively. Their
performance was then evaluated in a fair and transparent way to identify the state-of-the-art
amongst all submissions. Top ranked teams almost always employed deep learning tools, which
were predominately constitutional neural networks (CNNs). Despite the great efforts, lesion
outcome prediction remains a great challenge. The annotated data sets stays publicly available
and new approaches can be compared directly via the online evaluation system, serving as a
continuing benchmark (www.isles-challenge.org)},
	author = {Winzeck S. and Hakim A. and McKinley R. and Pinto J. and Alves V. and Silva C. and Pisov M. and Krivov E. and Belyaev M. and Monteiro M. and Oliveira A. and Choi Y. and Paik M. and Kwon Y. and Lee H. and Kim B. and Won J-H and Islam M. and Ren H. and Robben D. and Suetens P. and Gong E. and Niu Y. and Xu J. and Pauly J. and Lucas C. and Heinrich M. and Rivera L. and Castillo L. and Daza L. and Beers A. and Arbelaezs P. and Maier O. and Chang K. and Brown J. and Kalpathy-Cramer J. and Zaharchuk G. and Wiest R. and Reyes M.},
	date-added = {2018-12-10 09:49:40 +0100},
	date-modified = {2018-12-10 09:55:24 +0100},
	doi = {10.3389/fneur.2018.00679},
	issn = {1664-2295},
	journal = {Frontiers in Neurology},
	pages = {679},
	title = {ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI},
	url = {https://www.frontiersin.org/article/10.3389/fneur.2018.00679},
	volume = {9},
	year = {2018},
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	Bdsk-Url-1 = {https://www.frontiersin.org/article/10.3389/fneur.2018.00679},
	Bdsk-Url-2 = {https://doi.org/10.3389/fneur.2018.00679}}