## 3 Methods
### Segmenter
#### 3.1.3 Channel Spatial Attention Enhancement Module  
In our network architecture, the deepest feature map encapsulates global image information, where an attention mechanism is employed to enhance salient spatial and channel-wise features. Specifically, we integrate the Convolutional Block Attention Module (CBAM) [39] and train it end-to-end alongside the base network to prioritize critical regions and discriminative features while suppressing irrelevant background noise. The core principle of this approach lies in inferring attention weights along two orthogonal dimensions (channel and spatial) and subsequently applying these weights to the original feature maps through multiplicative modulation, thereby enabling adaptive feature refinement. This dual-path attention mechanism allows the model to dynamically emphasize informative features while attenuating redundant or distracting elements, leading to more efficient and discriminative feature representation. The Convolutional Block Attention Module consists of two sequentially arranged submodules: channel attention and spatial attention. The channel attention mechanism focuses on identifying and emphasizing the most informative channels by adaptively recalibrating channel-wise feature weights, thereby enabling more effective extraction of global contextual information. Distinct from the standard CBAM, which employs global pooling operations, we propose a multi-scale pooling strategy by incorporating average pooling with kernel sizes of 2 \(\times\) 2 and 4 \(\times\) 4. This approach captures both local and semi-global statistical information, thereby enhancing the model's capability to assess the importance of channel features across different spatial scales. The detailed procedure of the channel attention module is illustrated in Figure 4. Given an input feature map \(F\in R^{H\times W\times C}\), spatial compression is first performed in parallel using average pooling operations with two different kernel sizes (2 \(\times\) 2 and 4 \(\times\) 4), global average pooling, and max pooling, respectively generating four feature vectors F\({}_{1}\), F\({}_{2}\), F\({}_{3}\), and F\({}_{4}\), each having spatial dimensions smaller than those of the original feature map. Subsequently, these four feature vectors are separately fed into a shared multi-layer perceptron (MLP) to ensure parameter efficiency. The outputs of the MLP are channel descriptor vectors with spatial dimensions 1 \(\times\) 1 and channel dimension C. Finally, the four output feature vectors are summed element-wise and passed through a sigmoid activation function, yielding the channel attention weight vector \(M_{c}(F)\) with size 1 \(\times\) 1 \(\times\) C. The detailed computational pipeline is shown in Figure 4, with the mathematical formulation provided in Equations (4)-(8):  
\[F_{1}=pool_{avg}(F) \tag{4}\]  
\[F_{2}=pool_{2\times 2}(F) \tag{5}\]  
Figure 3: The attention module in the skip connection. \[F_{3} =pool_{4\times 4}(F) \tag{6}\] \[F_{4} =pool_{max}(F) \tag{7}\]  
\[M_{c}(F)=\sigma\Big{\{}MLP\Big{[}pool_{avg}(F_{1})\Big{]}+MLP\big{[}pool_{2\times 2 }(F_{2})\big{]}+MLP\big{[}pool_{3\times 3}(F_{3})\big{]}+MLP[pool_{max}(F_{4})]\Big{\}} \tag{8}\]  
In the equation, \(\sigma\) denotes the sigmoid activation function; the multi-layer perceptron is represented by the notation MLP; average pooling is denoted by the symbol \(pool_{avg}\); \(2\times 2\) pooling is denoted by the symbol \(pool_{2\times 2}\); \(4\times 4\) pooling is represented by the symbol \(pool_{4\times 4}\); and max pooling is indicated by the symbol \(pool_{max}\). The spatial attention mechanism, in contrast, is designed to focus on salient regions within feature maps by computing pixel-wise importance weights, thereby directing the model's attention towards critical spatial areas.