An empirical study on the effect of imbalanced data on bleeding detection in endoscopic video
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Bibliographic record
Abstract
In biomedical applications including classification of endoscopic videos, class imbalance is a common problem arising from the significant difference between the prior probabilities of different classes. In this paper, we investigate the performance of different classifiers for varying training data distribution in case of bleeding detection problem through three experiments. In the first experiment, we analyze the classifier performance for different class distribution with a fixed sized training dataset. The experiment provides the indication of the required class distribution for optimum classification performance. In the second and third experiments, we investigate the effect of both training data size and class distribution on the classification performance. From our experiments, we found that a larger dataset with moderate class imbalance yields better classification performance compared to a small dataset with balanced distribution. Ensemble classifiers are more robust to the variation in training dataset compared to single classifier.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 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 it