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Record W2167988080 · doi:10.2528/pier11050408

A BIMODAL RECONSTRUCTION METHOD FOR BREAST CANCER IMAGING

2011· article· en· W2167988080 on OpenAlexaff
Daniel Flores‐Tapia, Martin O’Halloran, Stephen Pistorius

Bibliographic record

VenueElectromagnetic waves · 2011
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of ManitobaCancerCare Manitoba
Fundersnot available
KeywordsElectrical impedance tomographyBreast cancerMicrowave imagingBreast imagingModality (human–computer interaction)MicrowaveIterative reconstructionRadarArtificial intelligenceComputer scienceComputer visionTomographyMedical physicsMammographyMedicineRadiologyCancerTelecommunications

Abstract

fetched live from OpenAlex

Breast Microwave Radar (BMR) has been proposed as an alternative modality for breast imaging. This technology forms a reflectivity map of the breast region by illuminating the scan area using ultra wide band microwave waveforms and recording the reflections from the breast structures. Nevertheless, BMR images require to be interpreted by an experienced practitioner since the location and density of the breast region can make the detection of malignant lesions a difficult task. In this paper, a novel bimodal breast imaging reconstruction method based on the use of BMR and Electrical Impedance Tomography (EIT) is proposed. This technique forms an estimate of the breast region impedance map using its corresponding BMR image. This estimate is used to initialize an EIT reconstruction method based on the monotonicity principle. The proposed method yielded promising results when applied to MRI-derived numeric breast phantoms.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2011
Admission routes1
Has abstractyes

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