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Record W2484736472 · doi:10.1016/j.media.2016.07.009

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

2016· article· en· W2484736472 on OpenAlexaff
Oskar Maier, Bjoern Menze, Janina von der Gablentz, Levin Häni, Mattias P. Heinrich‬, Matthias Liebrand, Stefan Winzeck, A. Basit, Paul Bentley, Liang Chen, Daan Christiaens, Francis Dutil, Karl Egger, Chaolu Feng, Ben Glocker, Michael Goetz, Tom Haeck, Hanna-Leena Halme, Mohammad Havaei, Khan M. Iftekharuddin, Pierre‐Marc Jodoin, Konstantinos Kamnitsas, Elias Kellner, Antti Korvenoja, Hugo Larochelle, Christian Ledig, Jia‐Hong Lee, Frederik Maes, Qaiser Mahmood, Klaus Maier‐Hein, Richard McKinley, John Muschelli, Chris Pal, Linmin Pei, Janaki Raman Rangarajan, Syed M. S. Reza, David Robben, Daniel Rueckert, Eero Salli, Paul Suetens, Ching‐Wei Wang, Matthias Wilms, Jan S. Kirschke, Ulrike M. Krämer, Thomas F. Münte, Peter Schramm, Roland Wiest, Heinz Handels, Mauricio Reyes

Bibliographic record

VenueMedical Image Analysis · 2016
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsPolytechnique MontréalUniversité de Sherbrooke
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute on AgingNational Cancer InstituteNortheastern UniversityNational Institute for Health and Care Research
KeywordsSegmentationBenchmarkingComputer scienceArtificial intelligenceStroke (engine)Multispectral imageBenchmark (surveying)Magnetic resonance imagingLesionMedicinePattern recognition (psychology)Machine learningRadiologyPathologyCartographyGeography

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.021
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0080.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0050.004
Research integrity0.0040.001
Insufficient payload (model declined to judge)0.0050.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.346
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations531
Published2016
Admission routes1
Has abstractno

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