AttSAM: Attention-Augmented Segment Anything Model for Accurate Polyp Segmentation

Polyp segmentation is crucial in the diagnosis of colorectal cancer. The introduction of the Segment Any-thing Model (SAM) provides powerful pretraining capabilities for polyp segmentation, but it faces two main challenges when applied to endoscopic images: first, its Transformer- based architecture tends to overlook local details, leading to feature bias; second, its performance on out-of-distribution (OOD) data is suboptimal, affecting prediction accuracy and confidence estimation. To address these issues, we propose an enhanced approach based on SAM that incorporates a Local Feature Enhancement Module (LFEM) and a Channel Attention Enhancement Module (CAEM). LFEM improves the capture of high-frequency information by enhancing local detail features, playing a crucial role in extracting polyp edges and textures. CAEM introduces a channel attention mechanism that dynamically adjusts the weight of feature channels, thereby enhancing the model's sensitivity and generalization ability. Additionally, we draw on the design principles of the Cross-Branch Feature Enhancement module and the Uncertainty- Guided Prediction Regularization (UPR) module to further improve SAM's performance in multi-scale feature extraction and on out-of-distribution data. Experimental results demon-strate that the inclusion of LFEM and CAEM significantly improves the model's segmentation accuracy on multiple public polyp datasets, particularly excelling in complex background scenarios.