Dual-Branch Multi-Task Regressor and Transformer Model for Endoscopic Image Classification
Bibliographic record
Abstract
Endoscopy plays a crucial role in the early diagnosis of colon cancer. The manual processing of images by skilled endoscopists is time-consuming, making automatic image classification highly valuable. We propose a novel multi-label classification method that integrates complementary learning from both local and global approaches. The model comprises a Swin Transformer branch for global feature extraction and a modified VGG16-based CNN branch for local feature analysis. The learning capability of the CNN branch is enhanced by concatenating a saliency map and the prediction of a texture feature vector through a multi-task learning framework. The proposed method outperformed state-of-the-art techniques, achieving an F1-score of 96.08% and an accuracy of 96.06% on the classification of the Kvasir-v2 dataset of endoscopic images.Clinical Relevance-Experimental results demonstrate the superiority of the proposed model for classifying endoscopic images, paving the way for enhanced diagnostic performance in clinical settings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".