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Record W2040838595 · doi:10.1109/tbme.2012.2220768

Microwave Radar and Microwave-Induced Thermoacoustics: Dual-Modality Approach for Breast Cancer Detection

2012· article· en· W2040838595 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 · 2012
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsMicrowave imagingThermoacousticsRadarModality (human–computer interaction)MicrowaveComputer scienceFinite-difference time-domain methodSet (abstract data type)Process (computing)AcousticsArtificial intelligencePhysicsOpticsTelecommunications

Abstract

fetched live from OpenAlex

Microwave radar and microwave-induced thermoacoustics, recently proposed as promising breast cancer detection techniques, each have shortcomings that reduce detection performance. Making the assumption that the measurement noises experienced when applying these two techniques are independent, we propose a methodology to process the input signals jointly based on a hypothesis testing framework. We present two test statistics and derive their distributions to set the thresholds. The methodology is evaluated on numerically simulated signals acquired from 2-D numerical breast models using finite-difference time-domain method. Our results show that the proposed dual-modality approach can give a significant improvement in detection performance.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.879
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 head0.011
GPT teacher head0.220
Teacher spread0.209 · 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