## 2 Related Works  
### Polyp Segmentation  
The segmentation of colorectal polyps is a classic problem in medical image segmentation. Its accurate and effective segmentation not only promotes the solution of other medical image segmentation problems, but also promotes the clinical practice of early colorectal cancer screening. Recent advances in deep learning have significantly enhanced the performance of polyp segmentation. PraNet [7], a pioneering framework using Res2 Net [12] as the backbone, was proposed to address multi-scale polyp variations. It enhanced segmentation accuracy on Kvasir-SEG through two key designs: (a) a Parallel Partial Decoder (PPD) for hierarchical feature aggregation, and (b) a reverse attention module for establishing correlation in boundary regions. However, its fixed pyramid levels limit its adaptability to extreme size variations. To alleviate the issues of color inconsistency and pixel imbalance in colonoscopy images, the authors of [13] designed a shallow attention gate to achieve precise localization of small polyps, and implemented probability calibration during the inference process to enhance the continuity of segmentation. However, manually designed color enhancement may degrade performance under low-light conditions. TGANet [14] demonstrated the feasibility of integrating polyp size and texture as text attention cues. It encoded ResNet50 features through text-guided spatial attention, effectively improving segmentation performance. This indicates that multimodal supervision can enhance the discriminability of features, but generating text in real-time remains technically challenging. Compared with traditional CNN-based models, Transformer-based models for medical image segmentation offer several advantages. CNNs rely on local receptive fields and convolutional operations, which can capture local features well but may have limitations in modeling long-range dependencies. In contrast, transformers, with their self-attention mechanism, can capture global context information more effectively. This is particularly useful in medical image segmentation, where understanding the global structure of organs or lesions is crucial. For example, in polyp segmentation, a polyp's context within the colon environment is important for accurate segmentation. Transformers can better capture the relationships between different parts of the polyp and the surrounding tissues, reducing the risk of missegmentation. Additionally, transformers can handle variable-sized input more flexibly, which is beneficial when dealing with medical images that may have different resolutions or cropping sizes. UViT-Seg [15] employed a visual transformer to extract long-range semantic information and utilized a convolutional neural network (CNN) module integrated with squeeze-and-excitation and dual attention mechanisms to capture low-level features in important regions of the image. The limited receptive field of conventional U-Nets in capturing global polyp patterns was addressed by [PERSON] et al. [16] through a hybrid architecture that synergistically combines multi-scale dense nested UNet with Transformer layers. HSNet [17] first employed a Transformer branch to capture long-range dependencies, then applied a CNN branch to capture local details of appearance, and finally bridged the gap between low-level and high-level features through an interaction mechanism, thereby enhancing the performance of polyp segmentation in this network. HIGF-Net [18] adopted a hierarchical guidance strategy to mine the deep global semantic information and shallow local spatial features of images. It also proposed an independent refinement module to refine the contour of polyps in uncertain areas, highlighting the differences between polyps and the background. The most advanced polyp segmentation algorithm, Polyp-PVT [19], abandoned the hierarchical sampling method based on CNN and introduced the Pyramid Vision Transformer to extract multi-scale features of polyps, effectively addressing the issue of poor edge segmentation accuracy.