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Record W2900568930 · doi:10.1049/cp.2018.0787

Recent Advancements in Time-Domain Breast Health Screening: Observations on the Phantom Stability and Wearable Hardware

2018· article· en· W2900568930 on OpenAlexaff
Lena Kranold, M. Perez Santa Maria, Mark Coates, M. Popoviü

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsWearable computerImaging phantomComputer scienceDomain (mathematical analysis)Wearable technologyEmbedded systemMedicineMathematicsNuclear medicine

Abstract

fetched live from OpenAlex

This work reports observations on the stability of tissue-mimicking breast phantoms over the course of three years in the context of microwave imaging and detection of breast cancer using the time-domain radar approach. The phantom properties are measured using a coaxial dielectric probe. Furthermore, an antenna array configuration, which has demonstrated to be favorable for our specific antenna design, has been experimentally tested with aged and newly fabricated phantoms. The results show that, over a three-year period, the higher-permittivity materials (those that mimic the glandular and tumorous tissue) exhibit more significant deterioration in their electric properties than those of low permittivity (e.g. fat-mimicking materials). This uneven deterioration impacts the dielectric contrast between the aged phantom tissues, the very essence of the hypothesis underpinning the field of microwave imaging. Hence, care must be exercised when aged tissue-mimicking phantoms are used in the controlled experiments for validation of devices intended for microwave breast tumor detection and imaging.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score0.534

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 head0.035
GPT teacher head0.253
Teacher spread0.218 · 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 designObservational
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

Citations6
Published2018
Admission routes1
Has abstractyes

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