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Record W1537405852

Improved tissue phantoms for experimental validation of microwave breast cancer detection

2010· article· en· W1537405852 on OpenAlexaff
Emily Porter, Jules Fakhoury, Razvan Oprisor, Mark Coates, Milica Popović

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsImaging phantomMicrowaveBreast cancerMicrowave imagingBreast tissueMaterials scienceBiomedical engineeringDielectricPermittivityCancer detectionDielectric permittivityComputer scienceCancerOptoelectronicsMedicineNuclear medicineTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

Abstract — This paper presents a methodology for constructing realistic breast phantoms for use in microwave detection of breast cancer. Phantoms for fat, skin, gland and tumour are made from a combination of everyday chemicals. The dielectric permittivity and conductivity of the phantoms are measured at microwave frequencies. Finally, we describe a procedure to combine the four phantom tissue types into a single breast phantom that is sufficiently realistic, both in terms of dielectric properties and physiological structure, to be used in experiments designed to assess the performance of microwave breast cancer detection techniques. Index Terms — Breast cancer detection, microwave imaging, tissue phantoms. I.

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)0.005
metaresearch head score (Gemma)0.006
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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.245
Teacher spread0.240 · 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
GenreMethods

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

Citations91
Published2010
Admission routes1
Has abstractyes

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