Stacking Ensemble Learning for Accurate Polyp Segmentation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Polyp segmentation in colonoscopy images is a crucial task for early detection and prevention of colorectal cancer. In this study, we propose an ensemble learning approach combining ResNet50-U-Net and ResNet50-Polyp, two fine-tuned deep learning models designed for gastrointestinal disease segmentation. The models were trained on the Kvasir-SEG dataset, and ensemble learning techniques were applied to enhance segmentation accuracy. Our approach demonstrates state-of-the-art performance, achieving an accuracy of 0.9689, precision of 0.9200, recall of 0.8800, F1-Score of 0.9065, and IoU of 0.7544. Comparative analysis with existing methods confirms the robustness and efficiency of our model in accurately identifying Polyp. The proposed ensemble model offers a reliable solution for Polyp segmentation, contributing to improved diagnostic in clinical settings.
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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 | 0.000 | 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 it