Quantifying Consistency of Microwave Breast Imaging: Laser Scanning for Assessing Breast Volume and Shape
Bibliographic record
Abstract
Microwave breast imaging is a promising approach that requires additional information such as the position, shape, and volume of the breast in the system for rigorous validation. The objectives of this proof-of-concept study were to develop a workflow to calculate the shape and volume of a breast positioned in contact with two imaging plates and to apply this workflow to assess the consistency of breast placement at sequential scans. The use of externally placed laser scanners facilitates capturing the shape and volume of the breast when positioned in the microwave system. A workflow was developed to estimate regions lacking observable measurements from the laser scanners, specifically implementing meshing, filtering, and surface estimation. The consistency of the breast shape and volume at sequential scans was quantified with the Dice coefficient, modified Hausdorff distance (MHD), and Fréchet distance. The study achieved an average Dice coefficient of 0.74 and MHD better than 10 mm, with the average below 4 mm. The Fréchet distances were higher than the MHD but demonstrated consistency with the phantom. Overall, this work demonstrates consistent placement of the breast at sequential scans and provides a framework for further investigation into the microwave signals and images.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".