# MSEANet: Multi-Scale Selective Edge Aware Network for Polyp Segmentation
## 2 Related Work
### Selective Edge Aware  
The structure of the SEA module is illustrated in Figure 3. During the decoding stage, the SEA module robustly fuses edge information across multiple scales, significantly enhancing the model's ability to delineate complex polyp boundaries. In polyp segmentation tasks, precise edge information is critical for improving segmentation performance. The SEA module leverages a reverse attention mechanism in conjunction with edge feature fusion, increasing the model's sensitivity to the target boundaries.  
Each SEA module processes the predicted map \(\textit{pred}_{i+1}\) generated by the deep supervision output of the previous layer, activating it with a sigmoid function to produce a probability map of salient regions. This probability map is inverted to generate a background attention map, which improves the model's focus on background areas. Subsequently, the background feature map is progressively fused with the global contextual feature map \(f_{i}^{c}\) from the CCF module, creating a refined feature map where salient regions are removed. To further enhance edge information, the SEA module integrates edge features \(f^{c}\) extracted by the EFE module. These edge features are concatenated with the background feature map, and the concatenated output is processed through a 3 \(\times\) 3 convolution operation to fully integrate multi-scale information, resulting in an enriched fused feature map \(f_{i}^{b}\). The mathematical calculations can be summarized as follows:  
\[f_{i}^{b}=F_{conv}\big{[}Concat\big{(}\big{(}\big{(}1-\sigma\big{(}\textit{ pred}_{i+1}\big{)}\big{)}\otimes\,f_{i}^{c}\big{)},f^{c}\big{)}\big{]} \tag{2}\]  
where \(f_{i}^{b}\) is the fused feature map, \(F_{conv}\) represents the 3 \(\times\) 3 convolution operation, \(\sigma\) is sigmoid function, and \(Concat\) denotes the concatenation operation. Subsequently, a simple gating mechanism is employed to adaptively weight the fused features, amplifying the model's response in critical areas and improving the localization of edge and shape information. A residual connection mechanism is then applied, where the weighted features are added element-wise to the original input features. This residual design preserves the global semantic information from the original features while enhancing local edge details. The calculation can be expressed as:  
\[\hat{f}_{i}^{b}=\Big{(}f_{i}^{b}\otimes\,\sigma\Big{(}f_{i}^{b}\Big{)}\Big{)} \bigoplus f_{i}^{c} \tag{3}\]  
Figure 3: Structure of Selective Edge Aware (SEA) module.  
where \(\hat{f}_{i}^{b}\) is the final output feature map, \(f_{i}^{b}\) represents the fused feature map, \(\otimes\) means element-wise multiplication, \(\bigoplus\) indicates element-by-element addition, and \(f_{i}^{c}\) denotes the original input features.  
By organically combining edge features, deep prediction features, and global contextual information, the SEA module can accurately capture the boundaries and shapes of polyps. This design effectively addresses the challenge of insufficient preservation of high-frequency information, thereby improving segmentation accuracy and robustness in complex scenarios.