ASAM2-UNet: An Attention-Enhanced SAM2 U-Net for Polyp Segmentation

To improve prompt utilization and boundary perception in SAM-based interactive medical image segmentation, this paper proposes ASAM2-UNet, a U-shaped visual foundation model built upon SAM2-UNet. Unlike methods that treat prompts as auxiliary spatial inputs, ASAM2-UNet explicitly incorporates prompt-derived priors into feature reasoning through an Interactive Prompt-Guided Focal Attention module. Specifically, a user-provided spatial prompt is converted into an explicit attention prior that modulates focal self-attention, allowing the network to emphasize target-relevant regions while retaining efficient local–global contextual modeling. In addition, a Contextual Semantic Information Complement module integrates multi-scale decoder features with uncertainty-aware and structure-aware cues derived from the initial prediction to refine ambiguous lesion boundaries. By integrating cross-layer spatial attention, local–global spatial fusion, and semantic-aware feature aggregation, the proposed module enhances the discrimination of ambiguous edges and complex foreground–background regions. Extensive experiments on five public polyp segmentation datasets demonstrate the effectiveness of the proposed method.