Stefan Winzeck, Arsany Hakim, Richard McKinley, José A. A. D. S. R. Pinto, Victor Alves, Carlos Silva, Maxim Pisov, Egor Krivov, Mikhail Belyaev, Miguel Monteiro, Arlindo Oliveira, Youngwon Choi, Myunghee Cho Paik, Yongchan Kwon, Hanbyul Lee, Beom Joon Kim, Joong-Ho Won, Mobarakol Islam, Hongliang Ren, David Robben, Paul Suetens, Enhao Gong, Yilin Niu, Junshen Xu, John M. Pauly, Christian Lucas, Mattias P. Heinrich, Luis C. Rivera, Laura S. Castillo, Laura A. Daza, Andrew L. Beers, Pablo Arbelaezs, Oskar Maier, Ken Chang, James M. Brown, Jayashree Kalpathy-Cramer, Greg Zaharchuk, Roland Wiest, Mauricio Reyes
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).
This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 April 2021.
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#4,016,532
of 23,103,436 outputs
Outputs from Frontiers in Neurology
#3,355
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Outputs of similar age
#78,849
of 337,955 outputs
Outputs of similar age from Frontiers in Neurology
#51
of 294 outputs
Altmetric has tracked 23,103,436 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,015 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has gotten more attention than average, scoring higher than 72% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 337,955 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 294 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.
