Statistical properties of 4000 raw and processed digital mammograms from a GE Senograph 2000D
Bibliographic record
Abstract
Optimization of the display of digital mammograms is an important challenge and requires knowledge of the characteristics of actual patient images. This work aims to create a description of some of the fundamental statistical properties of a large volume of images acquired on an FDA approved device as used in clinical practice. 4569 digital mammograms (1246 patients) were acquired between October 2001 and August 2002 on a GE Senograph 2000D at Sunnybrook and Women's College Health Sciences Centre. Images were saved in "raw" format. The breast was then segmented from the background on the image using a technique based on thresholding and some connectivity rules. The histogram of pixel values in the breast only is then calculated for both the raw and processed versions of the image. The region of constant thickness, where the breast is in contact with the compression paddle, was also segmented from the CC view raw images. The histogram and statistical properties in this central region were also calculated. Assorted statistical descriptors of the histograms were examined (dynamic range, mean, standard deviations, median and mode). The effect of image processing on the dynamic range in the periphery and central area of the breast was evaluated. The results were compared against the automatic exposure algorithm and acquisition parameters, projection (view) and breast thickness.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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 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".