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Record W2133308013 · doi:10.1109/tbme.2008.919883

Breast Surface Estimation for Radar-Based Breast Imaging Systems

2008· article· en· W2133308013 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRadarMicrowave imagingBreast imagingAntenna (radio)Computer scienceRadar imagingSurface (topology)Breast tumorComputer visionArtificial intelligenceMicrowaveRemote sensingMammographyElectronic engineeringAcousticsBreast cancerEngineeringTelecommunicationsMathematicsPhysicsGeologyMedicine

Abstract

fetched live from OpenAlex

Radar-based microwave breast-imaging techniques typically require the antennas to be placed at a certain distance from or on the breast surface. This requires prior knowledge of the breast location, shape, and size. The method proposed in this paper for obtaining this information is based on a modified tissue sensing adaptive radar algorithm. First, a breast surface detection scan is performed. Data from this scan are used to localize the breast by creating an estimate of the breast surface. If required, the antennas may then be placed at specified distances from the breast surface for a second tumor-sensing scan. This paper introduces the breast surface estimation and antenna placement algorithms. Surface estimation and antenna placement results are demonstrated on three-dimensional breast models derived from magnetic resonance images.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.196
Teacher spread0.189 · 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