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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{S}_{11}$</tex> 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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".