## 1 Introduction  
Medical image segmentation models are designed to process pathological images and delineate the shape and structure of lesion areas within the images. These models facilitate the rapid localization of lesions, thereby providing invaluable assistance in medical treatment [1, 2, 3]. U-Net [4] comprises three primary modules: the encoder, decoder, and skip connections. The encoder-decoder structure of U-Net represents a significant advantage. The encoder progressively reduces the image resolution to extract high-level semantic features, while the decoder progressively increases the resolution to restore these high-level semantic features to a segmentation result that approximates the original image size. This symmetrical structure enables the model to comprehend images at different scales, capturing overall semantic information and recovering detailed information. Skip connections facilitate the transfer of feature information from the encoder to the decoder, preserving local image details. This allows the decoder to utilize the richer, detailed features present in the encoder's early layers, thereby enhancing segmentation accuracy. The efficacy of the U-Net architecture in medical image segmentation is attributed to these advantages. Traditional U-Net architecture utilizes a convolutional neural network [5] (CNN) as an encoder to filter irrelevant information and downsample the feature map, preparing it for the subsequent stages where deep semantic information is extracted. Despite its success in medical image segmentation, reliance on CNNs limits the receptive field to a fixed convolution kernel size, hindering U-Net's ability to capture long-range pixel relationships withinthe feature map [6; 7; 8]. To address this limitation, [PERSON] et al. introduced Deformable Convolution and region of interest (RoI) pooling [9], which enhance spatial sampling locations with additional offsets, thereby improving the CNN's transformation modeling capabilities. Similarly, [PERSON] et al. employed a convolutional attention module to derive an attention map from both channel and spatial dimensions [10], enhancing adaptive feature extraction by multiplying this map with the input feature map. [PERSON] drew inspiration from the classical non-local means method in computer vision, integrating local operations within the CNN framework to establish a versatile module for capturing long-range dependencies [11]. [PERSON] proposed Dilated Convolution [12], enabling a CNN to regulate the computational resolution of its feature responses. Although these approaches alleviate some limitations of CNNs in capturing long-range relationships, they do not fundamentally resolve the underlying issues. With the advent of Transformer [13], researchers have begun applying this technique to medical image segmentation tasks to overcome CNN constraints [14; 15; 16; 17; 18]. The core feature of Transformer is its self-attention mechanism, which processes embedding information across all positions within the input sequence simultaneously, enhancing efficiency. Vision Transformer [19] (ViT) employs Transformer as an encoder, integrating it with U-Net to address limitations in capturing long-range pixel relationships. ViT divides the feature map into equally sized sub-maps, each encoded into a vector. During encoding, self-attention establishes inter-block relationships and captures comprehensive contextual information, with the encoded vector processed by the decoder to produce the labeled feature map. Although the Transformer architecture excels at processing global and long-distance semantic information, its encoding method can diffuse attention [20], potentially affecting computational efficiency. Consequently, several ViT variants [21; 22; 23; 24; 25] have been developed to address this issue.