Machine Learning based Reconstruction of Point-Like Scatterers in a Portable Microwave Detection Device
Bibliographic record
Abstract
Access to breast cancer screening is limited in low-income and remote areas, resulting in late-stage diagnosis and increased mortality rates. A portable microwave system was created by minimizing the device cost, size, and complexity. The small, cylindrical device features twenty-six patch antennas and inexpensive vector network analyzers operating from 0.7 - 3 GHz. A microwave radar model was modified to simulate S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</inf> measurements of the physical device. Radar simulations were performed on numerical phantoms consisting of two rod-like point scatterers with varying reflectivities of 10%, 30%, 50%, 70%, 90%, or 100%. A convolutional neural network (CNN) was trained to directly reconstruct the rod phantoms from their simulated S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</inf> sinograms. Despite the narrow bandwidth, the CNN could detect point scatterers with an accuracy up to 85%, improving on conventional resolving capabilities. Artificial intelligence microwave sensing methods offer promising possibilities for automated, low-cost breast cancer screening.
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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 | 0.001 | 0.001 |
| 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".