## 3 Methods
### Segmenter
#### 3.1.2 Attention Module  
In medical image segmentation tasks, the target regions often exhibit irregular shapes, varying sizes, and blurred boundaries, which cause traditional skip connections to introduce redundant information during feature fusion, thereby affecting segmentation accuracy. To enhance the model's ability to model critical information, we propose an improved attention mechanism that generates spatial attention maps by jointly leveraging the information from the attention signal Attentional_Signal and the local feature map DenseBlock_Out. The attention module contains two inputs: the attention signal \(\mathrm{G}^{\mathrm{D}}\) and the local feature map \(\mathrm{F}^{\mathrm{F}}\). We define the local feature map as \(\mathrm{F}^{\mathrm{c}}=\left\{\mathrm{f}_{\mathrm{i}}^{\mathrm{c}}\right\}_{ \mathrm{i=1}}^{\mathrm{M}}\), where \(\mathrm{f}_{\mathrm{i}}^{\mathrm{c}}\) represents the feature vector at the i-th pixel position in the feature map \(\mathrm{F}^{\mathrm{c}}\), with C channels. The attention signal is defined as \(\mathrm{G}^{\mathrm{D}}=\left\{\mathrm{g}_{\mathrm{j}}^{\mathrm{D}}\right\}_ {\mathrm{j=1}}^{\mathrm{N}}\), where \(\mathrm{g}_{\mathrm{j}}^{\mathrm{D}}\) represents the feature vector at the j-th pixel position in the feature map \(\mathrm{G}^{\mathrm{D}}\), with D channels. First, we apply a \(1\times 1\) convolution, batch normalization, and ReLU activation to the attention signal \(\mathrm{G}^{\mathrm{D}}\) to extract global contextual information and reduce dimensionality. Then, \(\mathrm{G}^{\mathrm{D}}\) is up-sampled to align its spatial dimensions with \(\mathrm{F}^{\mathrm{c}}\), resulting in channel-matched feature maps \(\mathrm{FM}_{1}\) and \(\mathrm{FM}_{2}\). Subsequently, \(\mathrm{FM}_{1}\) and \(\mathrm{FM}_{2}\) are element-wise added and passed through a ReLU activation to generate the spatial attention map. Finally, the generated attention map is multiplied element-wise with the original input feature map to obtain the attention-modulated output, denoted as Att-Output. The specific computation is shown in Equation (3).  
\[Att=Up(Sigmoid(W\times(Relu(W_{f}\times F^{c}+Up(W_{g}\times g^{\mathrm{D}})) ))) \tag{3}\]  
In the equation, \(W_{g}\) denotes a \(1\times 1\) convolution kernel with ccc filters; \(W_{f}\) represents a \(3\times 3\) convolution with a stride of 2; and \(W\) refers to a \(1\times 1\) convolution kernel with a single filter. The up-sampling operation, denoted as Up, utilizes bilinear interpolation to match the spatial dimensions. As a result, the generated attention map shares the same spatial resolution as the output of the dense module (DenseBlock_Out). Subsequently, the attention map is element-wise multiplied with DenseBlock_Out to produce the final output of the attention module.  
By performing weighted fusion of features across different hierarchical levels, the attention mechanism effectively captures critical target information at multiple scales while suppressing background noise and irrelevant regions. This enhances the saliency of the  
Figure 2: The structure of the dense convolutional module.  
target area and improves the network's ability to focus on and localize the region of interest. The specific attention module is illustrated in Figure 3.