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Evaluating System Design in Breast Microwave Sensing: Data and Image Quality in Multiple Systems

2024· article· en· W4395664635 on OpenAlexafffund
Tyson Reimer, Fatimah Eashour, Gabrielle Fontaine, Jordan Krenkevich, Stephen Pistorius

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsCancerCare ManitobaUniversity of Manitoba
FundersCanadian Cancer Society
KeywordsSoftware deploymentNoise (video)Systems designComputer scienceMicrowaveMicrowave imagingImage resolutionSignal-to-noise ratio (imaging)Resolution (logic)Breast imagingImage (mathematics)Breast cancerArtificial intelligenceAlgorithmTelecommunicationsCancerMedicineMammographySoftware engineering

Abstract

fetched live from OpenAlex

Numerous microwave-based breast imaging (MBI) systems have been developed to investigate the potential of microwave-based breast cancer detection. Despite the diversity in system design, relatively little research has compared systems and evaluated the impact of design parameters. This work evaluates the spatial resolution, noise, and shift-dependence of three imaging systems. These systems vary with respect to key design parameters and have been designed for different purposes - a bed-based system was designed for deployment to a permanent clinic, a bench-top system was designed for laboratory use, and a portable prototype system was designed for deployment to remote communities. The bed-based system was found to have the best spatial resolution with respect to both image and data quality, achieving a best-case resolution of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{12.4}\pm \mathbf{0.5})\mathbf{mm}$</tex> , and the best signal-to-noise ratio of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{26}\pm \mathbf{2})\ \mathbf{dB}$</tex> .

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)0.003
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.088
GPT teacher head0.323
Teacher spread0.235 · 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 designSimulation or modeling
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
Published2024
Admission routes2
Has abstractyes

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