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Record W4409164128 · doi:10.1017/s1759078725000376

Image quality analysis and comparison of three radar-based breast microwave sensing systems

2025· article· en· W4409164128 on OpenAlexafffund
Tyson Reimer, Gabrielle Fontaine, Fatimah Eashour, Jordan Krenkevich, Stephen Pistorius

Bibliographic record

VenueInternational Journal of Microwave and Wireless Technologies · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsCancerCare ManitobaUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceMicrowaveRadarRemote sensingMicrowave imagingTelecommunicationsGeology

Abstract

fetched live from OpenAlex

Abstract Several breast microwave sensing (BMS) systems have shown encouraging results as a potential breast cancer detection tool. The existing systems in the literature have diverse designs, equipment, measurement protocols, and analysis methods. However, there is relatively little investigation on the impact and performance of varying system designs. This work compares the impact of system design parameters on three existing BMS systems. The first system, a bed-based system, was designed for use in a permanent clinic. The second system, a bench-top system, was created for laboratory research. The third system, a portable system, was designed for use in low-income and remote communities. The bed-based system had the highest resolving capabilities, achieving a spatial resolution of 12.4 ± 0.5 mm. Additionally, the bed system had the highest signal-to-noise ratio of 26 ± 1 dB. The portable system had the least intensity dependence on polar positions within the imaging chamber. The bed system had the highest contrast between tumor- and adipose-mimicking materials. However, the contrast of tumor- and fibroglandular-mimicking materials was similar for each system. By comparing and evaluating the performance of multiple BMS systems, we improve our understanding of system design, allowing for potential studies into an ideal BMS system.

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 score0.265
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.012
GPT teacher head0.276
Teacher spread0.264 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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