Sci-PM Thurs - 09: A semianalytic model to extract differential linear scattering coefficients of breast tissue from energy dispersive x-ray diffraction measurements
Bibliographic record
Abstract
Our research group is focused on determining the potential applications of using x-ray diffraction signals to diagnosis breast cancer. We have built a custom made x-ray diffractometer system. Polyenergetic 50 kV beams collimated down to a 3 mm diameter are incident on 5 mm diameter 5 mm thick samples. A cadmium zinc telluride detector is positioned at an angle θ with respect to the center of the target. We use our semianalytic model coupled with energy dispersive x-ray diffraction measurements to extract differential linear scattering coefficients in units of m−1 sr−1. We optimize our system with as our target since good x-ray diffraction data is available. A 2-mm diameter aperture is positioned in front of our detector and the target to detector distance is ≈ 40 cm. We use a root-mean-square deviation to measure the overall agreement between our data and the gold standard. We get values of 1.6 and 2.0 m−1 sr−1 for scatter angles of 13° and 16° and values of 3.4, 2.5, 2.1, 2.8, 1.8 m−1 sr−1 for angles 5, 7, 8, 9, and 11. These values are obtained after correcting our data for fluorescence escape and hole tailing. However, the values are nearly the same even if we don't correct the data. At this stage, more optimization is required. Our preliminary results for breast tissue agree well with data measured by Kidane et al., Phys. Med. Biol. 44, 1791–1802 (1999). We intend to correlate the x-ray diffraction and cellular pathology signals of breast tissue.
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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) | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".