A volumetric method for estimation of breast density on digitized screen‐film mammograms
Bibliographic record
Abstract
A method is described for the quantitative volumetric analysis of the mammographic density (VBD) from digitized screen-film mammograms. The method is based on initial calibration of the imaging system with a tissue-equivalent plastic device and the subsequent correction for variations in exposure factors and film processing characteristics through images of an aluminum step wedge placed adjacent to the breast during imaging. From information about the compressed breast thickness and technique factors used for taking the mammogram as well as the information from the calibration device, VBD is calculated. First, optical sensitometry is used to convert images to Log relative exposure. Second, the images are corrected for x-ray field inhomogeneity using a spherical section PMMA phantom image. The effectiveness of using the aluminum step wedge in tracking down the variations in exposure factors and film processing was tested by taking test images of the calibration device, aluminum step wedge and known density phantoms at various exposure conditions and also at different times over one year. Results obtained on known density phantoms show that VBD can be estimated to within 5% accuracy from the actual value. A first order thickness correction is employed to correct for inaccuracy in the compression thickness indicator of the mammography units. Clinical studies are ongoing to evaluate whether VBD can be a better indicator for breast cancer risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| 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) | 0.008 | 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".