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Record W4206626051 · doi:10.1109/jerm.2021.3131029

Narrowband Microwave Breast Screening: Repeatability Study With Phantoms

2021· article· en· W4206626051 on OpenAlexaff
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
Fundersnot available
KeywordsNarrowbandRepeatabilityMicrowaveMicrowave imagingImaging phantomComputer scienceStandard deviationNoise (video)AcousticsAntenna (radio)ClutterArtifact (error)RadarPhysicsTelecommunicationsOpticsArtificial intelligenceMathematicsStatistics

Abstract

fetched live from OpenAlex

This work presents experimental results on measurement variability obtained with carbon-based tissue-mimicking phantoms using a prototype microwave breast cancer detection system on narrowband (2012.5 – 2100 MHz), adapted to use 16 flexible monopole antennas previously designed for ultrawideband (2 – 4 GHz). The use of narrowband permits a more accessible and compact system with the final goal of a wearable device for frequent scans, targeting early detection through tracking alterations on patients over time. This motivates the research of phantom measurement repeatability. Our narrowband system is described and results are presented for five measurement dates using two phantoms with skin layers and similar low percentages of glands, along with interchangeable plugs for all-fat and three tumor cases. Preliminary clutter rejection uses average trace subtraction on antenna pairs with same distance in-between. Significant signal distinction is noted between baseline and tumors on specific antenna pairs and some distinction is found on central tendencies over all signals. This is promising as the presence of skin and glands can heavily attenuate microwave signals. High variability is found on distinct measurement dates, increasing the standard deviation across all dates considerably. The measurement noise includes comparatively high and easier to remove systematic offsets as well as other confounders that occasionally increase standard deviation, which may mask signal features. This highlights the need to research further techniques to mitigate this variability between measurements in order to increase reliability of microwave breast screening devices and, even more generally, other biomedical devices based on similar principles.

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)0.001
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 score0.381
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.268
Teacher spread0.251 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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