Combination of enhanced AdaBoosting techniques for the characterization of breast cancer tumors
Bibliographic record
Abstract
Breast cancer is a life threatening disease affecting one of every eight women, with the risk increasing significantly with age. Non-invasive diagnostic modalities are preferable over biopsy. However, these non-invasive diagnostic techniques have lower diagnostic accuracy when compared to biopsy. Currently, CAD (Computer-Aided Diagnosis) techniques have demonstrated strong potential to increase the accuracy of such non-invasive diagnostic modalities. This paper proposes the use of boosting to increase the accuracy of CAD-based techniques to solve the breast cancer characterization problem. It investigates and compares the performance of popular variants of the boosting algorithms, namely: AdaBoosting, AveBoosting, GentleBoosting, ConserBoosting, and Average Conservative Boosting and their suitability for the breast cancer tumor characterization problem. This work also proposes a hybrid boosting algorithm that combines the advantages of several boosting techniques. The results of applying the different boosting techniques investigated on real breast cancer benchmarks show that the hybrid boosting algorithm outperforms the other boosting techniques on average by 48%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".