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Record W4245216946 · doi:10.1109/iembs.2006.4398395

Hybrid Microwave Tomography Technique for Breast Cancer Imaging

2006· article· en· W4245216946 on OpenAlexaff
Abas Sabouni, Daniel Flores‐Tapia, Sima Noghanian, Gabriel Thomas, Stephen Pistorius

Bibliographic record

VenueConference proceedings · 2006
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMicrowave imagingInverse scattering problemDielectricInverse problemPermittivityTomographyMammographyMicrowaveComputer scienceImage resolutionBreast cancerResolution (logic)Materials scienceOpticsScatteringPhysicsArtificial intelligenceCancerMathematicsTelecommunicationsMathematical analysisOptoelectronicsMedicine

Abstract

fetched live from OpenAlex

This paper addresses a two-dimensional inverse scattering method with a combination of tomography and radar methods for breast cancer detection. In order to rapidly construct high resolution images displaying the location, size, permittivity and conductivity of malignant tumors inside the body, the collected reflection from the scattered fields present in the scan area is segmented and their associated dielectric property maps are calculated. The dielectric profiles are obtained by using a technique that combines frequency domain finite difference time domain (FD) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TD analysis with genetic algorithm (GA) optimization. The applications of the proposed method can vary from medical imaging to nondestructive testing of materials and structures. The proposed technique yielded promising results when applied to simulated data

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.649
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 head0.006
GPT teacher head0.208
Teacher spread0.202 · 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 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

Citations0
Published2006
Admission routes1
Has abstractyes

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