Magnetic contrast-enhanced microwave biomedical imaging using discontinuous Galerkin contrast source inversion
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".