# An Enhanced Feature Extraction Network for Medical Image Segmentation
## 2 Related Works
[27] proposed triple U-Net for nuclear segmentation in pathological cancer. [PERSON] et al. [28] proposed Multi-scale U-Net (Mu-Net), which incorporates multiple U-Nets operating at different scales in parallel and each U-Net handles images at different scales. All of the aforementioned approaches enhance U-Net. However, they all ignore the impact of the relationship between network structures on specific tasks. In this paper, a cross-attention mechanism is used to select the features of skip-connection. SeNet [29] compresses each feature map in different channels into a single value to induct the network to learn a set of weights that reflect the importance of each feature map in their channel. The authors proposing GSoP-Net [30] and FeaNet [31] argued that using only global average pooling is insufficient, as it limits the modeling ability of the attention mechanism. GSoP-Net improves the squeeze module in SeNet and a global second-order pooling (GSOP) block is proposed to model high-order statistics while collecting global information. FeaNet reconsiders the global information captured from the perspective of compression, and analyzes the global average pooling in the frequency domain. The authors of these studies proved that the global average pooling is a special case of a discrete cosine transform. Building upon this insight, they proposed a novel multi-spectral channel attention. SRM (Style-based Recycling Module) [32] improves the squeeze module and excitement module by incorporating the mean and standard deviation of input features, thereby enhancing the network's capability to capture global information. It also uses a lightweight channel full connection layer (CFC) instead of the original full connection layer (FC) to reduce the computing requirements. GCT [33] comprises a general transformation unit for visual recognition tasks, which uses interpretable variables to visualize and model channel correlation. These variables determine the competitive or cooperative relationship between neurons and can be jointly trained with network parameters. ECANet [34] replaces the fully connection layer in SENet with one-dimensional convolution, which can replace the SE block well without adding additional parameters, and can also exceed the original performance of SE. Inspired by SENet, EncNet [35] contains a context encoding module, which is combined with the semantic encoding loss to model the relationship between the scene context and the probability of object categories, and then uses the global scene context information for semantic segmentation. There are many ways to combine an attention mechanism with U-Net. For example, [PERSON] et al. [36] proposed RA-UNet for segmenting CT images of liver tumors. This model obtains multi-scale attention information through the network to fuse shallow and deep features. In addition, [PERSON] et al. [37] proposed category attention boosting U-Net (CAB U-Net). [PERSON] et al. [38] proposed dual attention CapNet (DA-CapNet). In this paper, we use a cross-attention mechanism to filter out the noise from the encoder information as much as possible. This mechanism aims to enhance the network's ability to accurately identify the category of each pixel. Compared with the above methods, the cross-attention mechanism has a better ability to consider global information. Therefore, it can reduce the negative impact of complex backgrounds, fuzzy boundaries and small objects in medical image segmentation tasks. To sum up, deep CNN algorithms are widely used in the field of medical image segmentation tasks and have made great achievements. However, artificial intelligence in medicine image analysis is still a new field. The main challenges in the medical image analysis field are the lack of datasets and the imbalance of datasets. In this field, accurate auxiliary diagnosis is often crucial, as a wrong or inaccurate diagnosis can lead to delays in treatment or even exacerbate the illness. This paper presents an enhanced network derived from U-Net for medical image segmentation tasks.