Bibliographic record
Abstract
Canadian women have a one in nine chance of developing breast cancer during their lifetime. Mammography is the most common imaging technology used for breast cancer detection in its earliest stages through screening programs. Clusters of microcalcifications are primary indicators of breast cancer; the shape, size and number may be used to determine whether they are malignant or benign. However, overlapping images of calcifications on a mammogram hinder the classification of the shape and size of each calcification and a misdiagnosis may occur resulting in either an unnecessary biopsy being performed or a necessary biopsy not being performed. The introduction of 3D imaging techniques such as standard photogrammetry may increase the confidence of the radiologist when making his/her diagnosis. In this paper, traditional analytical photogrammetric techniques for the 3D mathematical reconstruction of microcalcifications are presented. The techniques are applied to a specially designed and constructed x-ray transparent Plexiglas phantom (control object). The phantom was embedded with 1.0 mm x-ray opaque lead pellets configured to represent overlapping microcalcifications. Control points on the phantom were determined by standard survey methods and hand measurements. X-ray films were obtained using a LORAD M-III mammography machine. The photogrammetric techniques of relative and absolute orientation were applied to the 2D mammographic films to analytically generate a 3D depth map with an overall accuracy of 0.6 mm. A Bundle Adjustment and the Direct Linear Transform were used to confirm the results.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".