Edge-guided Multi-scale Attention Fusion Network for gastrointestinal tumor image classification
Why this work is in the frame
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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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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