Evaluation of Differential Breast Microwave Imaging on MRI-Derived Breast Phantom Pairs
Bibliographic record
Abstract
Breast Microwave Imaging (BMI) has the potential to be used as an early breast cancer diagnostic tool, but its diagnostic performance is hindered by the strong reflections from the skin and anatomical differences between breasts. Differential imaging techniques can suppress common features such as the skin response and underlying breast tissue, displaying only differences between the left/right breasts such as a tumour. In this study, MRI-derived left and right breast phantoms were fabricated to examine the potential of differential imaging techniques on realistic breast exams. The delay-and-sum (DAS) and optimisation-based radar reconstruction (ORR) algorithms were used. Tumour responses were not identifiable after applying differential techniques to the realistic breast phantom pair. The influence of both breast translational misalignment and bilateral asymmetry was examined for differential BMI. When the contralateral breast was translationally shifted, tumour responses disappeared once the shift exceeded approximately 2 mm, yielding an AUC of 34.4%. Applying a simple temporal skin-alignment procedure restored strong tumour visibility and increased the AUC to 81.9%. For bilateral asymmetry, even slight differences in breast geometry—within the population’s normal volume asymmetry—produced an immediate reduction in the ability to detect tumour responses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".