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 S11measurements 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 S11sinograms. 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 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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".