## 2 Related Work  
Segmentation methods based on deep learning overcome the limitations of traditional manual feature segmentation and highlight the great potential of automatic feature learning. UNet, a deep learning network widely used in the field of medical image segmentation, mainly adopts a U-shaped encoding-decoding structure. This structure preserves the spatial detail information of shallow features by introducing skip connections between the encoder and decoder at each layer, thus achieving accurate segmentation of medical images. Although the UNet model performs well in medical image segmentation, its convolutional block, using a fixed kernel size, has a single receptive field and is unable to handle images at different scales. Architectures with specific kernel sizes may not be able to extract all relevant features from medical image datasets of various spatial resolutions. Secondly, while skip connections offer preliminary features for the decoder, constraints in its perceptual field, biases in convolutional operations, and integration of semantically diverse features may impact segmentation performance. Based on this, researchers began further investigations into the UNet [8], proposing several improvements to address these issues. Res-Unet [12] and Dense-UNet [16] replaced traditional convolutional blocks with Res-blocks from ResNet [2] and Dense-blocks from DenseNet [17], respectively. These modifications were aimed at more efficiently extracting semantic information, thereby enhancing the accuracy of image segmentation. [PERSON] et al. proposed an innovative multi-task [18], multi-scale learning framework aimed at predicting patient survival and responses. This framework utilizes a multi-task learning approach along with multi-scale feature extraction branches, allowing for the simultaneous extraction of rich information from both intratumoral and peritumoral regions, thereby improving the performance of radiomic analysis. The study utilizes shared representations across related tasks to improve learning efficiency and predictive performance while also providing methodological insights for other medical image analysis applications. Additionally, the DoubleU-Net [13] enhances the capture of contextual information by combining two UNet networks and employing a pool of Atrous spatial pyramids [19] to generate more accurate segmentation masks. It is important to acknowledge, however, that this stacking structure leads to significant increases in parameter count and computational requirements. Originally, the Transformer model [3] was developed primarily for machine translation tasks, demonstrating exceptional performance by utilizing self-attention mechanisms, layer normalization, feed-forward networks, and residual structures without relying on convolutional layers. The self-attention mechanism enabled the Transformer to excel in a variety of tasks. Given the success of Transformers, the field of image processing adopted the Vision Transformer (ViT) [20], which has garnered significant attention in the academic community due to its impressive capabilities in image recognition tasks. However, Transformers generally lack detailed localization information [21] and require more diverse datasets for training to achieve results comparable to those of CNN-based approaches. Networks such as TransUnet [22] and TransAttUNet [23] employ hybrid CNN-Transformer architectures for medical image segmentation, aiming to enhance feature extraction by combining the inductive biases of CNNs with the global contextual information processing strengths of ViT [20]. Conversely, SwinU-Net [24] represents a purely Transformer-based, UNet-like architecture for medical image segmentation that captures global and long-term semantic information through positional embeddings, hierarchical windowing, and attention mechanisms specific to Swin Transformers. These networks bear similarities to the UNet [8] architecture, particularly in terms of their comparable levels of skip connections, which may limit the effectiveness of feature fusion.