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Record W2006056707 · doi:10.1109/eucap.2014.6901806

Comparison of image quality metrics for electromagnetic wave propagation speed estimation in Breast Microwave Radar imaging scenarios

2014· article· en· W2006056707 on OpenAlexaff
D. Rodriguez-Herrera, Daniel Flores‐Tapia, Stephen Pistorius

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsCancerCare ManitobaUniversity of Manitoba
Fundersnot available
KeywordsMicrowave imagingImage qualityComputer scienceRadarBreast cancerMicrowaveModality (human–computer interaction)Artificial intelligenceComputer visionPattern recognition (psychology)Image (mathematics)TelecommunicationsMedicineCancer

Abstract

fetched live from OpenAlex

Breast cancer is one of the most common causes of death amongst women. The earlier the cancer is treated, the better the chances for recovery. One of most promising complimentary imaging modalities for breast cancer detection is Breast Microwave Radar (BMR). The spatial accuracy of BMR images is dependent on the wave propagation speed estimate used to reconstruct the recorded breast structure responses. If an erroneous estimate is used, the resulting images will be spatially inaccurate and may contain artifacts, which can compromise the confidence of the diagnosis. In this paper we compare the fitness of four different image quality metrics (Contrast, Entropy, Tenengrad and Laplacian) when used for electromagnetic wave speed estimation for BMR imaging scenarios. The simulations were done varying the permittivity of the targets and the speed propagation of the wave in the medium. It demonstrated that the use of image quality metrics is a viable tool for BMR electromagnetic wave speed estimation.

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: none
Teacher disagreement score0.803
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.278
Teacher spread0.262 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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