## 2 Related Work  
Colonoscopy allows doctors to detect and treat the disease early, significantly improving patient survival. However, it is challenging to determine the presence of colorectal polyps based on a colonoscopy alone. First, each pathological polyp contains multiple categories, for example, only common polyps include ulcerative, prominent, and invasive variants. Second, even within the same type of polyp, there is no uniform appearance. Finally, due to the frequently indistinct boundaries between polyps and surrounding mucosal tissue, this can result in problems of both under-segmentation and over-segmentation at the edges of segmentation results. This lack of stark contrast poses a challenge to segmentation methods.  
In computer vision tasks, the network responsible for extracting image features is often called the backbone network. AlexNet [6], using the convolutional neural network method, won first place in the ImageNet image classification task at the time. With the mining of deeper networks, ResNet [7] proposed by [PERSON] in 2015 has become one of the most widely used backbone networks in recent years. Due to ResNet's excellent performance, several networks built on its architecture have emerged, including ResNeXt [8], ResNeSt [9], and Res2 Net [10]. Several recent studies ([11; 12; 13; 14]) highlighted the importance of contextual information in improving a model's ability to predict high-quality segmentation results. Dilation 8 [15] uses a map-like multi-scale extended convolution layer to perform multi-scale context aggregation. DeepLab-v3 [16] uses the atrous spatial pyramid pool (ASPP) to capture useful contextual information across multiple scales. After this, DenseASPP [17] was introduced, which joins a set of convolution properties to generate multi-scale features.  
In segmentation methods for intestinal polyps, it is common to train a classifier to distinguish between polyps and surrounding tissue. However, these models often have a high error rate. The main reason behind this challenge is the obvious intra-class differences and relatively weak inter-class differences between polyp areas and their visually similar environment. For example, [PERSON] et al. [18] used a pre-trained model-based fully convolutional network (FCN) to identify and segment polyps, and [PERSON] et al. [19] used an improved FCN to improve the accuracy of polyp segmentation. Inspired by the successful application of U-Net [20] in biomedical image segmentation, U-NET++ [21] and ResUNet++ [22] were applied to the polyp segmentation task and good results were obtained. These methods focus on the segmentation of the entire polyp region but ignore the regional boundary constraint, which is the key to improving the segmentation performance. Therefore, Psi-Net [23] utilizes both region and boundary information in polyp segmentation but does not fully capture the relationship between the region and boundary. In addition, [PERSON] et al. [24] proposed a selective aggregation channel network with regional boundary constraints to segment polyps. PraNet [25] uses parallel reverse attention networks to accurately outline polyps and achieve good segmentation results. CaraNet [3] introduced the feature channel pyramid based on PraNet so that the network can learn the context information and improve the segmentation accuracy. BoxPolyp [26] uses box annotations to alleviate the over-fitting issue of previous polyp segmentation models, which generate a fine-grained polyp area through the iterative boosted segmentation model. RFPA [27] leverages feature propagation enhancement and feature aggregation enhancement modules for more efficient feature fusion and multi-scale feature propagation. CFA-Net [28] exploits hierarchical semantic information from cross-level features, which can characterize the cross-level and multi-scale information to handle the scale variations of polyps.