Edge-guided Multi-scale Attention Fusion Network for gastrointestinal tumor image classification
Bibliographic record
Abstract
Automated classification of gastrointestinal tumor images is a pivotal technology in computer-aided diagnostic systems. However, medical images are often affected by high-frequency noise due to limitations of acquisition devices and intrinsic tissue characteristics. In addition, lesion regions frequently exhibit blurred edges and low contrast, posing challenges for accurate extraction of discriminative features. To address these challenges, we propose a novel Edge-guided Multi-scale Attention Fusion Network (EdgeMAF-Net) for gastrointestinal tumor image classification. Specifically, we introduce a cross-stage partial fusion module that dynamically allocates features to both CNN and Transformer branches, enabling simultaneous modeling of local details and global context. This is complemented by a high-frequency attenuation and noise suppression mechanism, as well as a multi-scale edge attention calibration module, which integrates a three-stage enhancement strategy to capture features at different scales and delineate blurred boundaries. The module leverages a feature enhancement attention block to weight multi-source features, combined with a multi-scale edge enhancement block employing multi-scale pooling and edge extraction, and an adaptive gated fusion block to dynamically adjust feature fusion. EdgeMAF-Net outperforms existing methods in terms of accuracy, sensitivity, and boundary localization on the Chaoyang, Kvasir, and GasHisSDB datasets. Our code is available at https://github.com/Bambi-lab/EdgeMAF-Net .
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".