Radio Frequency Mammography Using Subtractive Imaging with Histogram of Oriented Gradients
Bibliographic record
Abstract
In this study, we explore the use of metasurfaces operating at submicrowave wavelengths in combination with Histogram of Oriented Gradients (HOG) techniques to detect breast cancer. By measuring the voltage magnitudes at the back of the unit cells of the metasurface, we generate images that reflect the electrical properties of an adipose breast phantom. We simulate two versions of a breast phantom: one without an anomaly, labeled healthy, and the other with an anomaly, labeled as unhealthy. The contrast between these two images was processed using the HOG technique to detect the anomaly. Our results demonstrate that this method can effectively detect small anomalies with a radius as small as 3 mm, highlighting the potential of the metasurface to produce images that capture the dielectric properties of the breast and the capability of HOG to identify the presence of tumors.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".