The application of Efron's bootstrap methods in validating feature classification using artificial neural networks for the analysis of mammographic masses
Bibliographic record
Abstract
Efron's bootstrap resampling method is used to analyze the performance of artificial neural networks (ANNs) in the area of feature classification for the analysis of mammographic masses. The purpose of feature classification in mammography is to discover the salient information that can be used to discriminate benign from malignant masses. The performance of ANNs is typically measured in terms of the area under the receiver operating characteristics (ROC) curve (A/sub z/). Performance uncertainty problems and the generalization problems of ANNs are still the critical issues that impede the further application of ANNs in clinical medicine. It is unreasonable and impractical to justify the performance of one ANN being better than another just by its best A/sub z/ value. Efron's bootstrap methods make it possible to quantitatively analyze the performance of ANNs and anticipate its change tendency with relatively high accuracy. Our experimental results show that the probability model of A/sub z/ is close to a normal distribution. The performance of ANNs is more sensitive to the change of topology than that of the size and the composition of the training set. Bootstrap methods can be used to find the optimal epochs and avoid overfitting.
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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 | 0.016 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".