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Microwave Breast Imaging System Characterization: Preliminary Healthy Volunteer Results

2023· article· en· W4388447876 on OpenAlexaff
Brendon C. Besler, Pedram Mojabi, Zefang Wang, Sarah Price, Zahra Lasemiimeni, Bryce A. Besler, Jeremie Bourqui, Bobbie Docktor, Elise Fear

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMicrowave imagingMicrowaveBreast cancerBreast imagingCharacterization (materials science)Medical physicsBiomedical engineeringPermittivityMammographyMedical imagingMedicineComputer scienceComputer visionArtificial intelligenceMaterials scienceCancerTelecommunicationsOptoelectronicsDielectricNanotechnology

Abstract

fetched live from OpenAlex

Microwave imaging has the potential to augment conventional breast imaging modalities by providing a point-of-care solution for frequent scanning during breast cancer treatment. A new ultrawideband, transmission-based microwave imaging system has been developed with a larger imaging area and more densely packed sensors than previous iterations. Preliminary results from a pilot study of healthy volunteers are presented. A qualitative exploration of the permittivity of the breast in reconstructed images shows that the system produces reproducible 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.934
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.007
GPT teacher head0.201
Teacher spread0.194 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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