Sci-Fri AM: Imaging - 05: Principal Component X-Ray Simulation Analysis of Breast Biopsy Phantoms
Bibliographic record
Abstract
Multivariate principal component analysis (PCA) was performed on x-ray simulations of breast biopsy phantoms. Semianalytic models were used to generate scatter (Ns) and photon transmission (N) signals from mixtures of fibrous (i), cancerous (j), and adipose tissue (k). A 5mm diameter 30 kV Mo beam is incident on a 5 mm thick biopsy. The incident exposure is 5.08 × 10−4 C/kg. An energy discriminating photon counting detector is assumed (e.g. CZT). For simulation of scatter measurements, the detector is placed at a scatter angle θ=10°, whereas for transmission θ=0°. The distance from the biopsy center to a 5 mm diameter aperture above the detector is 15 cm. The variables used in the PCA are Ns/N0 (16 keV), Ns/N0 (25 keV) and N/N0 (8 keV) where N0 is the incident spectrum. These variables are calculated 10 times. Each trial consists of biopsy shuffling of tissue blocks for a chosen fractional composition (i,j,k). A statistical component was incorporated in No. As a preliminary test to the classification model, a total of 66 different biopsies have been simulated and are used as unknowns. The PCA classes generated are based on a biopsy composed of 5 layers and 5 radii. The 66 unknown compositions were tested against the PCA classes to determine if the biopsy tissue composition could be identified. Of the 66 biopsies, 46 were correctly identified. An example of a misclassified unknown was for the composition (0,0.2,0.8) which was classified as (0.3,0,0.7). Further work is required to increase accuracy of identification.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".