Developing a quality control program for digital mammography: achievements so far and challenges to come
Bibliographic record
Abstract
Detection of breast cancers with mammography is a challenging task. Images must be of high quality if cancers are to be found at the earliest possible time. This motivates the need for a quality assurance program. It has long been recognized that the performance of a complex imaging system such as mammography can drift over time and, therefore, quality control procedures must be in place to ensure that all components of the imaging chain are operating properly. While digital mammography overcomes many of the technical limitations of screen-film mammography, its performance can easily be diminished if it is carried out in a suboptimal manner. Routine quality control is equally important for digital mammography as it was for screen-film imaging. While the need to monitor film processing generally disappears when digital imaging is employed, there are new requirements for quality control related to the display workstation and imaging software. Furthermore, to aid in controlling radiation dose to the breast, ...
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".