# A Medical Image Segmentation Network with Multi-Scale and Dual-Branch Attention
## 2 Related Work  
### Medical Image Segmentation  
In recent years, deep learning methods utilizing convolutional neural networks (CNNs) [19; 20; 21] have made significant progress in medical image segmentation. Notably, the fully convolutional network [22] has emerged as a potent tool in this domain, demonstrating considerable success. It is a type of encoder-decoder network structure that replaces traditional fully connected layers with fully convolutional layers to extract image feature information. This modification allows it to handle inputs of different sizes and shapes, making it more flexible. Res-UNet [23] integrates residual network concepts to mitigate issues related to gradient vanishing during training, thereby improving network convergence. Incorporating an attention mechanism into U-Net, Attention U-Net [12] enhances the network's ability to focus on relevant regions within the image during segmentation, leading to improved extraction and utilization of critical information. Attention U-Net++ [24] incorporates an attention mechanism into U-Net++ [25], allowing the network to simultaneously focus on global and local information; CFHA-Net [26] uses a triple hybrid mechanism to enhance the segmentation of polyp boundaries. BA-Net [27] effectively uses boundary details to provide supplementary information to enhance prediction capabilities through a small multi-task learning module learned jointly. R2U-Net [28] extracts multi-scale features through recursive residual blocks and then combines the attention mechanism to improve the focus on the key areas of the target. EIU-Net [29] utilizes feature extraction under different receptive fields and a normalized attention module to obtain multi-scale feature information while suppressing the influence of non-essential features on the results, thus improving the final segmentation outcomes. FSA-Net [30] fuses feature information of multiple scales through an attention mechanism and effectively aggregates important features. LM-Net [31] uses a multi-branch module to capture multi-scale features at the same level and then combines local and global self-attention modules to improve the problem of blurred segmentation boundaries. MSCA-Net [32] first leverages a multi-scale bridging module to fully utilize the features from both the encoder and decoder, and then uses a global-local channel-spatial attention module to emphasize significant features, thereby enhancing the segmentation results of medical images. However, these networks primarily emphasize local feature extraction while overlooking global semantic information. Therefore, to overcome the shortcomings of convolutional neural networks, transformers [14] are increasingly being adopted in image segmentation, offering a solution to the inherent limitations of convolutional neural networks. The Vision Transformer [33] split the image into pre-defined blocks and then apply a multi-head attention method to enhance the image's global understanding, hence increasing the image segmentation's effectiveness. The Swin Transformer [34] incorporates a shift window mechanism, facilitating interaction between different windows and empowering the model to efficiently grasp both the overarching and local details of the image. Swin-UNet [35] emerges from the fusion of the Swin Transformer with the U-shaped network, exhibiting advantages in handling large-scale images and multi-scale information. TDD-UNet [36] integrates the transformer's multi-head self-attention into the encoding layer of U-Net to extract comprehensive global context information. However, these networks are limited in extracting both local detail semantic information and global feature information at the same time and are prone to ignore important feature information. This paper proposes a new network structure that can simultaneously extract local detail feature information and global multi-scale feature information.