## 2 Materials and Methods
### The Basic Structure and Principle of ED Net
#### 2.2.5 DPCI_SE Block  
Double Pooling Concatenated Input SE Block (DPCI_SE Block) is based on the SE block, the key characteristic of which is to split channels and acquire the learning weights of each channel depending on the loss. This strategy lets the effective feature take up more learning weight and the ineffective or poor feature take up less learning weight so that the model can receive better training results. The process is divided into the squeeze phase, the excitation phase, and the reweight phase. The basic idea of the squeeze phase is to compress the feature input down the spatial dimension, use a global average pooling to improve the perceptual field, and match the dimension of the output with the number of input feature channels. The global information \(z_{{}_{C}}\) obtained from each channel feature map is expressed as:  
\[z_{{}_{C}}=F_{\text{sq}}(u_{c})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W} u_{c}(i,j) \tag{5}\]  
where \(u_{c}\) represents the feature map of each channel, \(H\) represents the height of the feature map, and \(W\) represents the width of the feature map. The basic idea of the excitation phase is based on the correlation of feature channels, each feature channel gets a weight, and the weight is used to indicate the importance of the feature channel. The input is the global information obtained in the Excitation phase, after a layer of a fully connected layer, ReLU activation function, fully connected layer for the excitation operation, and the sigmoid function to limit the number out results in the [0, 1] interval. The output learning weight \(s\) is expressed as:  
\[s=F_{ex}(z,W)=\sigma_{sigmoid}(W_{2}\sigma_{\text{ReLU}}(W_{1}z)) \tag{6}\]  
\(W_{1}\) and \(W_{2}\) represent the weight matrix of the two fully connected layers. \(W_{1}\in\mathbb{R}^{\widehat{\zeta}\times\mathbb{C}}\), \(W_{2}\in\mathbb{R}^{C\times\widehat{\mathbb{C}}}\), \(r\) is the decay factor, and the literature [21] states that this value is 16. Finally, reweight phase is to treat the weights obtained in the Excitation phase as the importance of this channel features, and weighs them to the previous features channel by channel. The important features are enhanced and the unimportant features are weakened. This operation can be expressed as:  
\[\tilde{x_{c}}=s_{c}u_{c} \tag{7}\]  
where \(u_{c}\) is the feature map of the original channel and \(s_{c}\) is the weight of the current channel obtained in the previous stage. The two are multiplied to obtain the enhanced or suppressed feature map \(\tilde{x_{c}}\). Then, the rescaling of the original feature maps in the channel dimension is completed by multiplying the channel by channel weighting onto the previous feature maps.  
Since the SE block was proposed, numerous academics have applied it and made improvements to it. In the literature [29], the authors introduced the SE block into their proposed block to improve the model's representation of channel attention. In this work, as shown in Figure 7, a DPCI_SE Block is constructed to add a dual pooling layer in front of the SE Block, to flow the feature map \(U\) into the parallel structure of the Avg pooling and the Max pooling, and to replace the ReLU in the SE Block with the GELU activation function.  
For the input feature map, the Avg pooling retains the global feature information and the Max pooling retains the local feature information, and finally, the feature information of the two parts is aggregated after the concatenating operation to obtain richer feature information. \(U^{*}\) This operation can be expressed as:  
\[U^{*}=[AvgPool(U),MaxPool(U)] \tag{8}\]  
This approach may attempt to improve the accuracy with which the spatial location information of the image is extracted in the attention module later.