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Record W3106706388 · doi:10.1109/tip.2020.3038363

PWD-3DNet: A Deep Learning-Based Fully-Automated Segmentation of Multiple Structures on Temporal Bone CT Scans

2020· article· en· W3106706388 on OpenAlexafffund
Soodeh Nikan, Kylen Van Osch, Mandolin Bartling, Daniel Allen, Sohrab Rohani, Ben Connors, Sumit Agrawal, Hanif M. Ladak

Bibliographic record

VenueIEEE Transactions on Image Processing · 2020
Typearticle
Languageen
FieldMedicine
TopicFacial Nerve Paralysis Treatment and Research
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSegmentationArtificial intelligenceComputer scienceTemporal boneComputer visionVoxelImage segmentationSmoothingPattern recognition (psychology)Anatomy

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)0.000
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.026
GPT teacher head0.318
Teacher spread0.292 · 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
GenreMethods

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

Citations60
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Image Processing→Same topicFacial Nerve Paralysis Treatment and Research→French-language works237,207→