Frequency Model Filtering for Microwave Imaging in Breast Cancer Applications
Bibliographic record
Abstract
In 2020, over 27,000 women were diagnosed with and treated for breast cancer in Canada. There are significant limitations with current imaging modalities being used in tracking recovery from treatment, such as ionizing radiation in x-ray mammography and accessibility for magnetic resonance imaging (MRI). Microwave imaging has shown to overcome these limitations with remarkable ability to distinguish between healthy and non-healthy tissue. The microwave imaging transmission system (MITS) developed at the University of Calgary can improve its results with filtering multipath data from the acquired frequency data. An interpolation modelling method is proposed to adapt with different variations in the microwave signals using polynomials of orders up to 15. The polynomial models show representation of the dominant signal’s shape and width with high statistical significance in the R squared and F- tests; therefore, providing reliable filtering parameters to create the microwave images.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".