Combining multiple feature representations and AdaBoost ensemble learning for reducing false-positive detections in Computer-aided Detection of masses on mammograms
Bibliographic record
Abstract
One of the drawbacks of current Computer-aided Detection (CADe) systems is a high number of false-positive (FP) detections, especially for detecting mass abnormalities. In a typical CADe system, classifier design is one of the key steps for determining FP detection rates. This paper presents the effective classifier ensemble system for tackling FP reduction problem in CADe. To construct ensemble consisting of correct classifiers while disagreeing with each other as much as possible, we develop a new ensemble construction solution that combines data resampling underpinning AdaBoost learning with the use of different feature representations. In addition, to cope with the limitation of weak classifiers in conventional AdaBoost, our method has an effective mechanism for tuning the level of weakness of base classifiers. Further, for combining multiple decision outputs of ensemble members, a weighted sum fusion strategy is used to maximize a complementary effect for correct classification. Comparative experiments have been conducted on benchmark mammogram dataset. Results show that the proposed classifier ensemble outperforms the best single classifier in terms of reducing the FP detections of masses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".