Use of CAD output to guide the intelligent display of digital mammograms
Bibliographic record
Abstract
For digital mammography to be efficient, methods are needed to choose an initial default image presentation that maximizes the amount of relevant information perceived by the radiologist and minimizes the amount of time spent adjusting the image display parameters. The purpose of this work is to explore the possibility of using the output of computer aided detection (CAD) software to guide image enhancement and presentation. A set of 16 digital mammograms with lesions of known pathology was used to develop and evaluate an enhancement and display protocol to improve the initial softcopy presentation of digital mammograms. Lesions were identified by CAD and the DICOM structured report produced by the CAD program was used to determine what enhancement algorithm should be applied in the identified regions of the image. An improved version of contrast limited adaptive histogram equalization (CLAHE) is used to enhance calcifications. For masses, the image is first smoothed using a non-linear diffusion technique; subsequently, local contrast is enhanced with a method based on morphological operators. A non-linear lookup table is automatically created to optimize the contrast in the regions of interest (detected lesions) without losing the context of the periphery of the breast. The effectiveness of the enhancement will be compared with the default presentation of the images using a forced choice preference study.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".