## 3 Methodology
### Mfa  
The MFAB module aims to learn the importance of each feature channel from multi-level feature maps without considering additional spatial dimensions. It can strengthen useful feature maps based on the importance of each channel and weaken feature maps that contribute less to the segmentation task. Specifically, it can describe the interdependence between feature channels in high-level and low-level feature maps. Advanced features have rich image semantic information, while low-level features that skip connections have more edge information. Low-level features are used to restore the details of an image.  
Figure 3: The structure of ECA model [36].  
Applying channel attention mechanisms to high-level and low-level features separately enabled us to increase the weight of key information in each channel while reducing the impact of irrelevant information.  
As shown in Figure 4, we first input the advanced features into the convolutional layers of \(1\times 1\) and \(3\times 3\). \(XH_{input}\) and \(XL_{input}\) have the same number of channels, \(V=[V_{1},V_{2},\ldots,V_{c}]\) is the set of convolutional kernels, where \(V_{c}\) is the parameter of the \(c\)-th convolution kernel. We were able to calculate output \(U=[u_{1},u_{2},\ldots,u_{c}]\):  
\[u_{c}=v_{c}*X_{input}=\sum_{i=1}^{c}{(v_{c}^{i})*x^{i}} \tag{3}\]  
where \(v_{c}=[v_{c}^{1},v_{c}^{2},\ldots,v_{c}^{c}]\) and \(X_{input}=[x^{1},x^{2},\ldots,x^{c}]\), \(X_{input}\in\)(\(XH_{input}\) or \(XL_{input}\)). The \(*\) here represents convolution.  
Then, the global average pooling is utilized to compress each feature into a numerical column of \(1\times 1\times C\), and channel level statistical information is generated. Formally, the statistical \(S_{c1}\) and \(S_{c2}\) are obtained by reducing the feature maps \(XH_{input}\) and \(XL_{input}\). The calculation method for the \(c\)-th element of \(S_{1}\) and \(S_{1}\) is:  
\[S_{c1}=F_{L}(XL_{input})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}{u_{c} (i,j)} \tag{4}\]  
\[S_{c2}=F_{H}(XH_{input})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}{u_{c} (i,j)} \tag{5}\]  
\(H\) and \(W\) represent height and width, respectively, and \(u_{c}\) represents the feature maps of each channel. Then, a bottleneck layer with two fully connected (FC) layers and an activation function is used to reduce the complexity of the model and capture channel dependencies \(z_{1}\) and \(z_{2}\).  
\[z_{1}=F_{Ls}(S_{1},P)=\delta_{1}(P_{1}\delta_{2}(P_{2},S_{1})) \tag{6}\]  
\[z_{2}=F_{Hs}(S_{2},P)=\delta_{1}(P_{1}\delta_{2}(P_{2},S_{2})) \tag{7}\]  
Figure 4: The structure of MFAB. MFAB uses two SE-Blocks to capture low-level and high-level feature maps, respectively. The final channel attention feature map is obtained through Concat connections. FC represents the fully connected layer. ReLU represents the Rectified Linear Unit.  
here, \(P_{1}\) and \(P_{2}\) represent fully connected layers, and \(\delta_{1}\) and \(\delta_{2}\) represents the sigmoid function and ReLU activation function, respectively. Then, we combine the channel outputs of low-level and high-level features using functions.  
\[z=F_{add}(\cdot)=z_{1}+z_{2} \tag{8}\]  
\(XH_{output}\) is obtained by rescaling T with activated V.  
\[\overset{\sim}{X}H_{output-c}=F_{scale}(T_{c},V_{c})=V_{c}T_{c} \tag{9}\]  
The final output \(X_{output}\) of MFAB is obtained by two \(3\times 3\) convolutional layers that capture semantic information.