Polyp segmentation by global cues and multi-level features
In colonoscopy,automatic polyp segmentation is a prerequisite for developing computer-aided colonoscopy detec-tion and diagnosis systems.Polyp segmentation is a highly challenging task due to the high similarity between polyps and surrounding tissues,as well as the significant variability in polyp size and shape.To address these challenges,this paper proposes a polyp segmentation method based on global clue localization and multi-view feature fusion.A global clue localiza-tion module is designed to propagate global positional information to each level of feature maps,explicitly embedding posi-tional knowledge of camouflaged polyps into the features.Additionally,a self multi-view feature fusion module is developed to capture hierarchical features across different views,enabling better adaptability to various polyp segmentation scenarios.The proposed method outperforms PraNet by margins of 1.2,3.3,1.8,8.5,and 3.7 percentage points on five datasets,demonstrating its effectiveness in terms of learning and generalization capabilities.
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