Clinical Report: Adaptive Gated Fusion for Multiclass Detection of GI Disorders

Overview

This study presents a hybrid transformer framework for the detection of gastrointestinal diseases from endoscopic images. The model integrates local and global features through an adaptive gated fusion mechanism.

Background

Gastrointestinal diseases pose a significant health burden, necessitating accurate and timely detection for effective management. Endoscopic imaging is crucial for identifying mucosal abnormalities, yet interpretation can be challenging due to operator dependency and variability. Advances in deep learning, particularly hybrid transformer models, offer solutions to improve diagnostic consistency.

Data Highlights

The proposed framework was evaluated on a multi-class gastrointestinal endoscopic dataset.

Key Findings

• The hybrid transformer framework integrates global reasoning from ViT-L/32 and spatial modeling from MaxViT-L.
• Adaptive gated fusion dynamically regulates local and global feature contributions on a per-sample basis.
• The model outperformed several state-of-the-art transformer and hybrid architectures.
• XAI analyses confirmed lesion-focused activation consistency.

Clinical Implications

The findings indicate that hybrid transformer architectures can enhance the accuracy of gastrointestinal disease classification.

Conclusion

The study presents adaptive local-global feature integration in improving diagnostic performance and interpretability for gastrointestinal disorders.

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