One-Stage Attention-Centric Instance Segmentation Framework for Gastrointestinal Disease Localization in Endoscopic Images

Accurate instance segmentation of gastrointestinal (GI) diseases in endoscopic images remains challenging due to visual ambiguity, diffuse lesion boundaries, and class imbalance. This study presents a one-stage, attentioncentric instance segmentation framework for the automated localization and classification of gastrointestinal diseases and anatomical landmarks in endoscopic imaging. The model employs residual feature aggregation and attention-enhanced representations and is trained using transfer learning with extensive preprocessing and data augmentation to improve generalization. Experimental results demonstrate stable convergence and strong performance, achieving an overall mAP@0.5 of 0.856, with…