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Record W2061757288 · doi:10.1109/aps.2014.6904589

Evaluation of the skin thickness effects in Breast Microwave Radar images

2014· article· en· W2061757288 on OpenAlexaff
Mario Solis Nepote, Diego Rodriguez Herrera, Jorge Hernando Sacristán, Daniel Flores Tapia, Stephen Pistorius

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMicrowave imagingRadarMicrowaveModality (human–computer interaction)Breast cancerNon-ionizing radiationRadar imagingComputer scienceMedical physicsBiomedical engineeringBreast imagingMammographyRadiologyMedicineArtificial intelligenceCancerTelecommunicationsOpticsPhysicsInternal medicine

Abstract

fetched live from OpenAlex

Breast Microwave Radar is a novel technology for breast cancer detection. Its relative low cost and the use of non-ionizing radiation makes it a prospective complimentary imaging modality. Recent clinical trials have proven the capability of this technology to detect small lesions, however, additional work is required to optimize several parameters of this technology to increase its use in routine clinical imaging procedures. In this study, the effect of the skin thickness in microwave radar images is evaluated using experimental datasets recorded from realistic breast phantoms. Preliminary results suggest that tumors responses can be resolved in scenarios where the skin layer is as thick as 2mm.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.211
Teacher spread0.206 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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