TIME-DOMAIN MICROWAVE RADAR APPLIED TO BREAST IMAGING: MEASUREMENT RELIABILITY IN A CLINICAL SETTING
Bibliographic record
Abstract
Abstract—This work presents an evaluation of the measurement challenges in clinical testing of our microwave breast cancer screening system. The time-domain radar system contains a multistatic 16-antenna hemi-spherical array operating in the 2–4GHz frequency range. We investigate, for the first time with such a system in clinical trials, the repeatability of measurements and its effect on image reconstruction. We record vertical and horizontal measurement uncertainties under different scenarios and verify using previously introduced compensation methods that they can be successfully reduced to an acceptable level from the standpoint of image reconstruction. We also examine how placement of an immersion medium can affect collected breast scan data. Finally, we probe the repeatability and consistency of measurements with patients. With the goal of confirming the feasibility of frequent breast health monitoring, with our system, we obtain a total of 342 breast scans collected over 57 patient visits to determine how much scan data varies when there are no changes in between scans, and how much it varies when the patient is repositioned in the system. We confirm that, by taking care in patient positioning in the system and with respect to the immersion medium, the measurement repeatability is high. 1.
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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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".