Mammographic thickness compensation for image analysis and display enhancement
Bibliographic record
Abstract
This paper proposes a novel algorithm for mammographic image enhancenment, based on identifying the peripheral region of the breast and suppressing the large change in signal caused by reduction of thickness there, while maintaining the local contrast information related to tissue composition. The thickness compensation algorithm consists of three processing steps. The first step is to generate a thickness map using two phantoms, one which simulates the shape of the breast in the cranio-caudal projection and a second one as a triangular attenuator. The second step is to warp the phantom thickness map in the peripheral region to that of the breast image. The third step is to equalize the signal values in the peripheral region relative to the signal in the uniform thickness area using the warped thickness map data. Examples are presented to show the effectiveness of the proposed method in effectively suppressing the large range of signal caused by thickness changes in the peripheral region, thereby facilitating image presentation and analysis. The performance of the proposed algorithm was also evaluated on clinical mammograms by computing volumetric breast density.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".