A Data-Driven Method for Minimizing the Positioning Errors in Breast Microwave Sensing
Bibliographic record
Abstract
Prior knowledge of the tissue locations is required to determine if an algorithm can accurately locate a tumour in breast microwave sensing (BMS). The localization error, defined as the distance between the actual and reconstructed tumour locations, has been used to quantify the accuracy of a reconstruction algorithm in BMS. However, the actual tumour location may be affected by systematic setup positioning errors, presenting a challenge to the use of this metric. This work focuses on quantifying and minimizing the positioning error and the uncertainty in the tumour's location. Experimental$\mathrm{S}_{11}$measurements were performed using a 2-9 GHz breast microwave sensing system. 113 scans were performed using a point-like metal object placed at various locations within the system. The target positions were extracted using both image-based and time-domain methods. The systematic positioning errors were determined by comparing the reconstructed and actual object positions. The time-domain method was observed to reduce the localization error compared to the image-based method. Accounting for the setup positioning errors reduced the positioning uncertainty from ±7.00 mm to ±2.06 mm.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".