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Estimating Breast Composition with Microwave Permittivity and Attenuation

2025· article· W4417132279 on OpenAlexaff
Pedram Mojabi, Jeremie Bourqui, Roger Y. Tsang, Elise Fear

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAttenuationMicrowaveBreast densityPermittivityBreast imagingMicrowave imagingMammography

Abstract

fetched live from OpenAlex

Breast density is a significant risk factor for breast cancer. Women typically learn about their breast density only during their first mammogram. In this work, we explore a safe, comfortable, and portable microwave imaging device to estimate breast composition. Both the permittivity and attenuation of the breast are reconstructed, and these parameters are used to estimate breast composition. This paper presents results from three volunteers with available mammograms, each exhibiting different breast densities. Our findings demonstrate that both permittivity and attenuation vary with breast density. Specifically, as breast density increases, localized regions with higher permittivity and attenuation become more pronounced. This study highlights the potential of microwave imaging to provide valuable insights into breast composition, offering an early and accessible tool for breast health assessment.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.878
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 head0.005
GPT teacher head0.213
Teacher spread0.208 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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