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Magnetic contrast-enhanced microwave biomedical imaging using discontinuous Galerkin contrast source inversion

2015· article· en· W1939214682 on OpenAlexaff
Cameron Kaye, Ian Jeffrey, Joe LoVetri

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMicrowave imagingMicrowaveDielectricMaterials scienceNuclear magnetic resonanceMagnetic fieldPermittivityContrast (vision)Computer sciencePhysicsArtificial intelligenceOptoelectronicsTelecommunications

Abstract

fetched live from OpenAlex

Microwave imaging (MWI) continues to steadily progress towards clinical application as a low-cost complementary imaging tool for breast cancer detection and treatment monitoring. Contrast-enhanced MWI is a relatively new extension to the research field which employs exogenous agents to improve resulting reconstructions by artificially accentuating the complex dielectric or magnetic property variation between healthy and cancerous tissues. Although a handful of microwave contrast agent studies have been carried out focusing primarily on modifying the permittivity of the targeted tissue (S.C. Hagness et al., IEEE Trans. BME, 57, 8, 1831–1834, 2010), recent investigations of magnetic nano-particles (MNP) have been of particular interest, since they augment the magnetic permeability of the region in which they accumulate. As a dearth of magnetic material exists naturally in the human body, MNP-enhanced MWI allows the detection of targeted induced magnetic anomalies within otherwise purely dielectric biological tissues, using the electromagnetic response produced by clusters of retained MNPs (O.M. Bucci et al., IEEE Trans. Biomed. Eng., 58, 9, 2528–2536, 2011). To the authors' knowledge the only published work reporting quantitative images of magnetic polarizability has used synthetic breast data, with inversions based on the truncated singular value decomposition (TSVD) scheme (R. Scapaticci et al., IEEE Trans. Biomed. Eng., 61, 4, 1071–1079, 2014).

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 categoriesMeta-epidemiology (narrow)
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 score0.873
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.213
Teacher spread0.202 · 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.

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

Citations1
Published2015
Admission routes1
Has abstractyes

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