MétaCan
Menu
Back to cohort
Record W2167988080 · doi:10.2528/pier11050408

A BIMODAL RECONSTRUCTION METHOD FOR BREAST CANCER IMAGING

2011· article· en· W2167988080 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

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.665

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 head0.008
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