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Record W4407212784 · doi:10.1109/jerm.2025.3531693

Quantifying Consistency of Microwave Breast Imaging: Laser Scanning for Assessing Breast Volume and Shape

2025· article· en· W4407212784 on OpenAlexafffund
Carina M. Butterworth, Pedram Mojabi, Elise Fear

Bibliographic record

VenueIEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology · 2025
Typearticle
Languageen
FieldMedicine
TopicInfrared Thermography in Medicine
Canadian institutionsUniversity of Calgary
FundersAlberta Cancer Foundation
KeywordsMicrowave imagingBreast imagingConsistency (knowledge bases)MicrowaveLaser scanningVolume (thermodynamics)Breast cancer3d scanningMammographyBiomedical engineeringLaserMaterials scienceMedicineOpticsMedical physicsComputer sciencePhysicsArtificial intelligenceInternal medicineTelecommunications

Abstract

fetched live from OpenAlex

Microwave breast imaging is a promising approach that requires additional information such as the position, shape, and volume of the breast in the system for rigorous validation. The objectives of this proof-of-concept study were to develop a workflow to calculate the shape and volume of a breast positioned in contact with two imaging plates and to apply this workflow to assess the consistency of breast placement at sequential scans. The use of externally placed laser scanners facilitates capturing the shape and volume of the breast when positioned in the microwave system. A workflow was developed to estimate regions lacking observable measurements from the laser scanners, specifically implementing meshing, filtering, and surface estimation. The consistency of the breast shape and volume at sequential scans was quantified with the Dice coefficient, modified Hausdorff distance (MHD), and Fréchet distance. The study achieved an average Dice coefficient of 0.74 and MHD better than 10 mm, with the average below 4 mm. The Fréchet distances were higher than the MHD but demonstrated consistency with the phantom. Overall, this work demonstrates consistent placement of the breast at sequential scans and provides a framework for further investigation into the microwave signals and 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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.027
GPT teacher head0.334
Teacher spread0.307 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations3
Published2025
Admission routes2
Has abstractyes

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