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Record W2072424584 · doi:10.1109/iceaa.2012.6328662

Spatial sampling requirements for multistatic Breast Microwave Radar imaging

2012· article· en· W2072424584 on OpenAlexaff
Daniel Flores‐Tapia, Oleksandr Maizlish, Stephen Pistorius

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsCancerCare ManitobaUniversity of Manitoba
Fundersnot available
KeywordsMicrowave imagingComputer scienceSampling (signal processing)Multistatic radarModality (human–computer interaction)Radar imagingBreast imagingSynthetic aperture radarMicrowaveRadarBistatic radarComputer visionArtificial intelligenceBreast cancerMammographyTelecommunicationsCancer

Abstract

fetched live from OpenAlex

Breast Microwave Radar is a novel imaging modality for early stage cancer detection. In the last couple of years, multistatic imaging protocols have been used for BMR due to their increased specificity and sensitivity. Nevertheless, several aspects of this data acquisition methodology need to be optimized so they can be used in clinical scenarios. In this paper, a novel mathematical model of the spatial sampling constraints for a BMR cylindrical multistatic scan geometry is proposed. This model was derived by analyzing the bounds of the spherical phase function of a generic multistatic scan geometry. The validity of the model was evaluated using numeric phantoms that mimic the dielectric properties of breast tissues. The results are consistent with mathematical model, suggesting that the model presented in this paper can be used to assist in the design of multistatic BMR systems.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.866
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.022
GPT teacher head0.262
Teacher spread0.240 · 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
GenreMethods

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

Citations0
Published2012
Admission routes1
Has abstractyes

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