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A Methodology for Evaluating Image Resolution in Experimental Breast Microwave Imaging

2023· article· en· W4378842178 on OpenAlexaff
Tyson Reimer, Stephen Pistorius

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsCancerCare ManitobaUniversity of Manitoba
Fundersnot available
KeywordsImage resolutionComputer scienceImage qualityArtificial intelligenceContrast (vision)Microwave imagingComputer visionRadar imagingRadarNoise (video)Resolution (logic)Image (mathematics)Pattern recognition (psychology)MicrowaveTelecommunications

Abstract

fetched live from OpenAlex

Breast microwave sensing (BMS) is a potential method for breast cancer detection. While radar-based imaging methods have been applied in patient studies, the evaluation of radar-based imaging methods has been limited. Image quality metrics, which aim to quantitatively describe the quality of an image, have been generally limited to descriptors of image contrast. Contrast is only one aspect of image quality - the traditional image quality aspects of image resolution, noise, contrast resolution, accuracy, and artifacts have not been fully addressed in the BMS literature. This work describes methodologies and quantitative metrics for evaluating spatial resolution in BMS. These methods have been applied to evaluate the quality of images produced by the delay-and-sum (DAS), delay-multiply-and-sum (DMAS), and optimization-based radar reconstruction (ORR) methods. The spatial resolution of images produced by the DAS and DMAS beamformers was found to be (1.95 ± 0.15) cm and by the ORR algorithm to be (1.65 ± 0.15) cm.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.067
GPT teacher head0.355
Teacher spread0.288 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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