## 3 Proposed Method
### Double-Flow Linear Pooling Enhancement Unit  
U-Net utilizes jump connections to combine shallow and deep semantic features. Nonetheless, the straightforward fusion method is susceptible to generating semantic gaps due to feature disparities. In order to tackle this issue, we optimized the jump connections and introduced the double-flow linear pooling enhancement unit (DLE). As illustrated in Figure 5, the DLE unit employed double-flow paths to establish cross-channel dependencies and gather a broader range of contextual information.  
For the input feature map \(F_{in}\in\mathbb{R}^{H\times W\times C}\), we applied deep convolution with a \(3\times 3\) convolution kernel and an expansion ratio of 2 to process the input feature map, resulting in a new feature map \(F_{in}{}^{\prime}\in\mathbb{R}^{H\times W\times}\). This operation, as opposed to standard convolution, captured feature map information across a larger range of sensory fields without introducing any additional parameters.  
\[F_{in}{}^{\prime}=DwConv_{k=3}^{rate=2}(F_{in}) \tag{4}\]  
where \(DwConv_{k=3}^{rate=2}\) denotes deep convolution with a kernel size of \(3\times 3\) and an expansion ratio = 2.  
Subsequently, we conducted max pooling and average pooling on \(F_{in}{}^{\prime}\) to extract more comprehensive channel information and generate feature maps \(F_{ap}\in\mathbb{R}^{1\times 1\times C}\) and \(F_{mp}\in\mathbb{R}^{1\times 1\times C}\), respectively.  
\[F_{ap} =Avgpool\left(F_{in}{}^{\prime}\right) \tag{5}\] \[F_{mp} =Maxpool\left(F_{in}{}^{\prime}\right)\]  
Figure 4: The structure of the cascaded adaptive feature extraction unit (CAE).  
Figure 5: The structure of the double-flow linear pooling enhancement unit (DLE).  
Finally, we employed the softmax function to normalize the weights of both \(F_{ap}\) and \(F_{mp}\) along the channel dimension. The outcome is two new attention views obtained by multiplying these weight matrices with \(F_{in}{}^{\prime}\). These two views are concatenated along the channel dimension, resulting in a concatenated feature map with dimensions H \(\times\) W \(\times\) 2C. Following this, dimensionality reduction is executed using the linear function \(W_{\mu}\), and the outcome is input into the decoder. The precise methodology is as follows:  
\[F_{out}=W_{\mu}\big{(}Concatenate\big{(}F_{in}{}^{\prime}\times\sigma\big{(} F_{ap};F_{mp}\big{)}\big{)}\big{)} \tag{6}\]  
where \(\sigma(\cdot)\) signifies the \(sigmoid\) function, \(F_{out}\) represents the output feature map resulting from channel-wise concatenation, and the linear function \(W_{\mu}\) is implemented through a \(1\times 1\) convolution operation. This convolution operation serves the purpose of reducing the channel dimensions of the feature map, which aids in the subsequent feature fusion process.  
The double-flow linear pooling enhancement unit integrates shallow and deep features in a \"progressive\" manner. It simultaneously feeds the generated contextual information and the original encoder features into the decoder. In addition to diminishing the semantic information gap between different pathways, the DLE unit strengthens information exchange between the encoder and decoder pathways, leading to enhanced model stability. Moreover, the features extracted by this unit have a beneficial impact on the localization of target regions. Furthermore, the deep convolution and pooling operations partially reduce both the parameter count and computational load.