Experimental Phase and Dielectric-Based Comparison of Propagation Speed in Microwave Breast Imaging
Bibliographic record
Abstract
Microwave Breast Imaging is a promising imaging modality that leverages differences in the dielectric properties between breast tissues to provide affordable and reliable breast cancer screening. Accurate image reconstruction requires the microwave propagation speed in the tissues. The propagation speed can be obtained by analyzing the phase of the wave transmitted through the media. An experimental bistatic geometry was used to collect$S_{21}$data for various tissue-mimicking liquids. The propagation speeds were extracted using the phase-based method and were compared to those obtained from dielectric measurements of the materials. The propagation speed was underestimated across the liquids, and a linear frequency-dependent correction was applied above 4 GHz to minimize the effects of noise. The Concordance Correlation Coefficient (CCC) and Pearson Correlation Coefficient (PCC) analysis showed good agreement for both DGBE 95% and DGBE 70% with CCC over 0.920 and PCC over 0.982, followed by DGBE 90 %. Glycerin was associated with the lowest values of CCC and PCC, 0.849 and 0.906, respectively, but there was still reasonable agreement between the theory and the experimental values. This study demonstrates the potential of the phase-based method to enhance MBI image reconstruction, moving away from the homogeneous approach and towards more precise, data-driven techniques.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".