Improvement of Multi-Frequency Microwave Breast Imaging through Frequency Cycling and Tissue-Dependent Mapping
Why this work is in the frame
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Bibliographic record
Abstract
Two modifications of the Contrast Source Inversion (CSI) algorithm have been employed to improve resulting 2-D microwave images of the dielectric properties of synthetic breast models produced using a well-established frequency-hopping technique. First, while reconstructions from low-frequency data are often used as initial guesses for otherwise unstable higher-frequency inversions, an improvement in overall image quality can be observed when the imaginary part of the reconstructed low-frequency image is altered to reflect the identical tissue geometry as its real part when used as an initial guess during subsequent reconstructions. Second, in such frequency-hopping scenarios, rather than terminating reconstructions following inversions of the highest frequency data, “frequency cycling” (i.e., returning to the lowest frequency in the sequence) can be used to further improve reconstructions without loss of resolution or anatomical detail.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it