## 2 Materials and Methods
### Method
#### 2.4.4 Semantic Decoder
As shown in Table 2, a large-sized convolution kernel can be decomposed into 2 to 3 smaller convolution operations, and this decomposition method can achieve theoretical receptive fields of 15 and 23, respectively. Decomposed convolutions can generate multiple feature maps with different receptive field sizes, providing greater flexibility for subsequent kernel selection. Compared to directly using large-sized convolution kernels, the sequential decomposition method can significantly reduce computational complexity. While maintaining the same theoretical receptive field, the decomposition strategy can not only reduce the model's parameter count but also effectively lower the network's computational overhead. The specific calculation process is shown in Equation (6). \[U_{0}=X,U_{i+1}=\mathcal{F}_{i}(U_{i}) \tag{6}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Receptive Field (RF)** & **Convolution Kernel and Dilation Rate (K,D) Sequence** \\ \hline  & (15,1) \\
15 & (3,1) \(\rightarrow\) (5,3) \\  & (3,1) \(\rightarrow\) (7,2) \\ \hline  & (23,1) \\
23 & (5,1) \(\rightarrow\) (7,3) \\  & (3,1) \(\rightarrow\) (5,2) \(\rightarrow\) (5,3) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Examples of Receptive Field Decomposition. Figure 9: Structure Diagram of LSK Module. where \(\mathcal{F}_{i}(\cdot)\) represents the \(i\)-th convolution operation with dilation rate, and \(U_{i}\) denotes the \(i\)-th input feature map. Additionally, each small-sized convolution kernel is followed by a \(1\times 1\) convolution layer FF to reduce the number of channels, thereby decreasing subsequent computational and parameter costs. The calculation process is shown in Equation (7). \[U_{0}=X,U_{i+1}=\mathcal{F}_{i}(U_{i}) \tag{7}\]  
where \(\widetilde{U}_{i}\) represents the feature map after processing through the \(1\times 1\) convolution layer \(\mathcal{F}_{i}^{1\times 1}(\cdot)\). In the fusion phase, the contextual features obtained from different receptive field convolution kernels in the decomposition phase are first concatenated along the channel dimension. The calculation process is shown in Equation (8). \[\widetilde{U}=Concat(\widetilde{U_{1}},\dots,\widetilde{U_{i}}) \tag{8}\]  
where \(\widetilde{U}\) represents the feature map after concatenating \(\widetilde{U_{i}}\). Then, channel-based average pooling and max pooling are applied to \(\widetilde{U}\) to effectively extract spatial relationships between different positions in the feature map. The calculation process is shown in Equation (9). \[SA_{avg}=P_{avg}(\widetilde{U}),SA_{\text{max}}=P_{\text{max}}(\widetilde{U}) \tag{9}\]  
where \(P_{avg}(\cdot)\) and \(P_{max}(\cdot)\) represent channel-based average pooling and max pooling, respectively, and \(SA_{avg}\) and \(SA_{max}\) represent the spatial relationship feature maps after average pooling and max pooling. To achieve information interaction between different spatial relationships, different spatial relationship feature maps are concatenated, and the convolution layer \(\mathcal{F}^{2\to N}\) is used to transform the aggregated feature map (2 channels) into two \(N\times N\) spatial attention maps (where \(N\) is the width of the feature map, assuming input feature dimensions are consistent). The calculation process is shown in Equation (10). \[\widehat{SA}=\mathcal{F}^{2\to N}\big{(}[SA_{avg},SA_{max}]\big{)} \tag{10}\]  
where \(\widehat{SA}\) represents the set of spatial attention maps. For each spatial attention map \(\widehat{SA_{i}}\), a sigmoid activation function is used to obtain the spatial selection mask \(\widehat{SA_{i}}\) for each participating decomposed convolution kernel. The calculation process is shown in Equation (11). \[\widehat{SA_{i}}=\sigma(\widehat{SA_{i}}) \tag{11}\]  
where \(\sigma(\cdot)\) represents the sigmoid function. In the selection phase, the feature maps in the decomposed convolution kernel sequence are first weighted according to the spatial selection masks.