Field nonuniformity correction for quantitative analysis of digitized mammograms
Bibliographic record
Abstract
Several factors, including the heel effect, variation in distance from the x-ray source to points in the image and path obliquity contribute to the signal nonuniformity of mammograms. To best use digitized mammograms for quantitative image analysis, these field non-uniformities must be corrected. An empirically based correction method, which uses a bowl-shaped calibration phantom, has been developed. Due to the annular spherical shape of the phantom, its attenuation is constant over the entire image. Remaining nonuniformities are due only to the heel and inverse square effects as well as the variable path through the beam filter, compression plate and image receptor. In logarithmic space, a normalized image of the phantom can be added to mammograms to correct for these effects. Then, an analytical correction for path obliquity in the breast can be applied to the images. It was found that the correction causes the errors associated with field nonuniformity to be reduced from 14% to 2% for a 4 cm block of material corresponding to a combination of 50% fibroglandular and 50% fatty breast tissue. A repeatability study has been conducted to show that in regions as far as 20 cm away from the chest wall, variations due to imaging conditions and phantom alignment contribute to <2% of overall corrected signal.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".