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Record W3172211302 · doi:10.1109/jerm.2021.3084126

Skin Phantoms for Microwave Breast Cancer Detection: A Comparative Study

2021· article· en· W3172211302 on OpenAlexafffund
Lena Kranold, Jasmine Boparai, Leonardo Fortaleza, Milica Popović

Bibliographic record

VenueIEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrowaveMaterials scienceBiomedical engineeringImaging phantomBreast cancerHuman breastSkin thicknessDielectricHuman skinBreast tissueMedicineComputer scienceOptoelectronicsCancerNuclear medicine

Abstract

fetched live from OpenAlex

The advancement of microwave radar prototypes for breast cancer detection purposes requires stable tissue-mimicking materials (phantoms) that dielectrically represent the breast tissues and allow for repeated experiments in a controlled laboratory environment. In this study, we compare the dielectric properties of three different skin phantoms to assess their suitability for prototype testing in the frequency range of 0.5–10 GHz. First, we verify the properties of two polyurethane-based fat-mimicking phantoms. Then, we evaluate the skin phantoms in larger blocks and as 2-mm thin layers. Finally, we conduct two separate experiments with the 2-mm skin phantoms layered over the two different fat phantoms. All the results are compared to dielectric properties of excised human skin tissue reported in the literature.

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 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: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.027
GPT teacher head0.317
Teacher spread0.290 · 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.

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

Citations10
Published2021
Admission routes2
Has abstractyes

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