Image quality analysis and comparison of three radar-based breast microwave sensing systems
Bibliographic record
Abstract
Abstract Several breast microwave sensing (BMS) systems have shown encouraging results as a potential breast cancer detection tool. The existing systems in the literature have diverse designs, equipment, measurement protocols, and analysis methods. However, there is relatively little investigation on the impact and performance of varying system designs. This work compares the impact of system design parameters on three existing BMS systems. The first system, a bed-based system, was designed for use in a permanent clinic. The second system, a bench-top system, was created for laboratory research. The third system, a portable system, was designed for use in low-income and remote communities. The bed-based system had the highest resolving capabilities, achieving a spatial resolution of 12.4 ± 0.5 mm. Additionally, the bed system had the highest signal-to-noise ratio of 26 ± 1 dB. The portable system had the least intensity dependence on polar positions within the imaging chamber. The bed system had the highest contrast between tumor- and adipose-mimicking materials. However, the contrast of tumor- and fibroglandular-mimicking materials was similar for each system. By comparing and evaluating the performance of multiple BMS systems, we improve our understanding of system design, allowing for potential studies into an ideal BMS system.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".