Conformal mesh and two-step tomographic imaging of the Supelec breast phantom
Bibliographic record
Abstract
The realistic breast phantom developed by the French team at Supelec provides an excellent opportunity to explore various measurement systems and associated algorithms. In this case there is a known ground truth for the geometry of the segmented regions for direct comparison of images with actual sizes, shapes and locations of the different features. It also allows for testing utilizing a range of dielectric materials for both the adipose and fibroglandular region which can be useful given the disparity of published property values. For this experiment, we are exploring our tomographic imaging algorithm with a log transformation in the context of restricting the imaging zone strictly to that space occupied by the phantom. In this case, the 2-dimensional perimeter of the breast was approximated by a simple ellipse which was extracted from the original image. All images were recovered without the assistance of a priori information and also included our 2-step imaging scheme. Simple parameter tests were performed to assess the limitations of the technique, especially when the imaging zone was not accurately determined. The final results were also analyzed by examination of histograms of the field residuals to assess whether the algorithm is robust and unbiased.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".