Enhanced Physics Modelling in Radar-Based Microwave Imaging for Breast Cancer Detection
Bibliographic record
Abstract
Microwave-based breast imaging (MBI) is an emerging modality that may serve as a screening tool due to the relatively large dielectric contrast between malignant and healthy tissues, the relatively low cost and small size of microwave hardware, and the favourable safety profile of non-ionizing microwave imaging. After more than two decades of research into MBI and several published clinical trials, challenges remain before the modality can be used clinically. Existing estimates of the diagnostic specificity are relatively low, between 20–65%. As a result of the limited specificity of the technique, existing radar-based image reconstruction algorithms have not demonstrated sufficient accuracy for breast cancer diagnosis. This article proposes using enhanced physics modelling (EPM) to improve the accuracy of the physics models used in image reconstruction to address the limited diagnostic accuracy. The results obtained in this study indicated that using EPM significantly improved the area under the curve (AUC) of the receiver operating characteristic curve. The AUC was improved from (84$\pm$1)% to (92$\pm$1)% through the use of EPM, demonstrating the potential of physics-informed radar-based image reconstruction in MBI.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".