MétaCan
Menu
← Back to cohort
Record W3006530269 · doi:10.1007/s12021-019-09439-6

Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change

2020· article· en· W3006530269 on OpenAlexfundno aff
Cassidy M. Fiford, Carole H. Sudre, Hugh Pemberton, Phoebe Walsh, Emily N. Manning, Ian B. Malone, Jennifer M. Nicholas, Willem H. Bouvy, Owen Carmichael, Geert Jan Biessels, M. Jorge Cardoso, Josephine Barnes

Bibliographic record

VenueNeuroinformatics · 2020
Typearticle
Languageen
FieldComputer Science
TopicMedical Image Segmentation Techniques
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchGenentechNational Institutes of HealthIXICOH. Lundbeck A/SCentre For Medical Engineering, King’s College LondonServierEisaiUniversity of Southern CaliforniaWellcome TrustUniversity College LondonNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchNational Institute on AgingNational Institute for Health and Care ResearchNorthern California Institute for Research and EducationPfizerBiogenBioClinicaF. Hoffmann-La RocheAlzheimer's SocietyNovartis Pharmaceuticals CorporationU.S. Department of DefenseEli Lilly and CompanyBristol-Myers SquibbAlzheimer's Disease Neuroimaging InitiativeMeso Scale DiagnosticsAlzheimer's AssociationWolfson FoundationBrain Research TrustPennington Biomedical Research FoundationFoundation for the National Institutes of Health
KeywordsHyperintensityArtificial intelligenceBayesian probabilityComputer scienceSegmentationCognitionFeature selectionSelection (genetic algorithm)Pattern recognition (psychology)Machine learningPsychologyMedicineMagnetic resonance imagingNeuroscience

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.015
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.034
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.312
Teacher spread0.256 · 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 designSimulation or modeling
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

Citations25
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueNeuroinformatics→Same topicMedical Image Segmentation Techniques→French-language works237,207→