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Record W2152038938 · doi:10.1109/iembs.2009.5333347

A heterogeneous breast phantom for microwave breast imaging

2009· article· en· W2152038938 on OpenAlexaff
Majid Ostadrahimi, R. Reopelle, Sima Noghanian, Stephen Pistorius, Arman Vahedi, Faezeh Safari

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsImaging phantomMicrowave imagingMicrowaveDielectricMaterials scienceElasticity (physics)Biomedical engineeringPenetration depthOpticsComputer sciencePhysicsOptoelectronicsMedicineTelecommunicationsComposite material

Abstract

fetched live from OpenAlex

Recently there has been significant attention given to imaging biological tissues using microwave radiation. In order to verify microwave imaging algorithms, realistic body models are needed to measure and simulate the penetration of microwave energy into the tissue and to reconstruct the image. We have created a phantom which has dielectric properties that are close to the properties of the real breast tissue. The phantom includes materials that accurately simulate the dielectric properties of skin, fat, gland and tumor tissues while providing good contrast of conductivity. The phantom is fabricated from materials that are widely available and is easy to make. In addition the elasticity of the materials enables the phantom to be shaped into two dimensional (2D) or three dimensional (3D) forms.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.003

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 head0.205
Teacher spread0.200 · 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

Citations17
Published2009
Admission routes1
Has abstractyes

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