## 1 Introduction  
Melanoma is the most severe and fatal form of skin cancer [1], and dermatologists diagnose pigmented skin lesions and malignant melanomas through dermoscopy. While physicians currently employ computer-aided diagnosis (CAD) [2] and utilize robotics [3, 4] to enhance surgical precision and accuracy for melanoma treatment, the segmentation of lesion regions often still necessitates manual labeling and review by experienced clinicians [5]. Furthermore, the intricate shapes and complexities of skin lesions (e.g., small sizes, intricate forms, and blurred boundaries due to color and texture) pose challenges for CAD systems based on traditional image segmentation algorithms, particularly in meeting the demands of precise image segmentation in the context of big data. Recently, deep learning-based approaches have exhibited significant advantages in enhancing segmentation accuracy over traditional methods based on thresholding, edge detection, and region- or graph-based techniques, particularly in the realms of medical and complex image segmentation. Deep learning-based image segmentation methods can handle various types of image data. Among them, the U-Net convolutional neural network [6] has shown good performance in image segmentation. The network utilizes an encoder to extract features of different scales from the image and a decoder with skip connections to obtain a larger receptive field. Therefore, many subsequent medical image segmentation models have been implemented based on U-Net. Inspired by U-Net, UNet++ [7] introduces a series of nested dense convolutional blocks. This connection bridges the semantic gap between the encoder and decoder before feature fusion. In addition, new skip connection methods, such as residual connections [8] and dense connections [9], are also introduced into the network architecture. Although convolutional neural networks have become the mainstream method in medical image segmentation, these networks using convolutional operations inevitably have limitations in modeling long-range dependencies due to the local nature of inductive biases and weight sharing. Moreover, CNN-based segmentation methods still face difficulties in modeling and extracting global-level semantic features [10]. The emergence of Transformers can effectively address the aforementioned issues. The Transformer is the first sequence-to-sequence prediction model to be used for NLP tasks [11]. Due to its ability to fully utilize the global information of input sequences and effectively model the global context, it has gradually gained widespread attention from computer vision researchers. Among them, the Swin Transformer [12] is one of the better-performing networks. The Swin Transformer addresses the computational and memory overhead issues of traditional Transformers when dealing with large-scale images by introducing a window-based attention mechanism. To overcome the limitations of local self-attention mechanisms in handling long-range dependencies, the Swin Transformer innovatively adopts the shift-window operation, dividing the image into multiple overlapping windows instead of uniformly partitioning it. Each window is processed as an independent image block, and there can be overlapping regions between windows. In this way, the Swin Transformer avoids performing global self-attention calculations on the entire image. [PERSON] et al. [13] proposed residual afterglow normalization technology and the scaled cosine attention method based on the Swin Transformer, effectively transferring low-resolution pre-trained models under windows to corresponding models with higher resolutions. Additionally, in medical image processing tasks, to overcome the difficulties of convolutional networks in effectively extracting local features, the Swin-Net segmentation network, proposed by [PERSON] et al. [14], maintains global semantics while fully utilizing the underlying features of the network. Currently, many semantic segmentation tasks require high-resolution input images or large attention windows.