## 2 Materials and Methods
### Space and Channel Reconstruction Convolution (SCConv)
#### 2.2.1 Spatial Reconstruction Unit (SRU)  
SRU enhances feature representation in the spatial dimension and suppresses invalid spatial information by separating redundant spatial features and reconstructing valid information. The separation operation aims to distinguish those feature maps that are rich in information from those that have less spatial content information. The SRU structure is shown in Figure 3.  
Using the parameters of the trainable GN (group normalization) layer, it is possible to evaluate the variance of different feature maps over spatial pixels. Subsequently, by implementing the operations of reweighting and reconstruction, the feature maps are able to be separated and reconstructed for the purpose of optimizing feature representation. Specifically, given a particular intermediate feature map \(X\in R^{N\times C\times H\times W}\), where represents the \(N\) batch dimension, \(C\) represents the channel dimension, and \(H\) and \(W\) represent the height and width of the spatial dimension, respectively. The process starts by normalizing the input feature \(X\). First, normalize the input features \(X\) by subtracting the mean \(\mu\) and dividing it by the standard deviation \(\sigma\). The computational process can be formulated as Equation (1):  
\[X_{out}=GN(X)=\gamma\frac{X-\mu}{\sqrt{\sigma^{2}+\epsilon}}+\vartheta \tag{1}\]  
where \(\mu\) and \(\sigma\) are the mean and standard deviation of \(X\), \(\epsilon\) is a small positive constant added for division stability, and \(\gamma\) and \(\vartheta\) are trainable affine transformations. The variance of spatial pixels in each batch and channel is measured using the trainable parameter \(\gamma\in R^{C}\) in the \(GN\) layer. Richer spatial information represents more variance for larger \(\gamma\) spatial pixels. The normalized weights \(W_{\gamma}\in R^{C}\) represent the importance of different feature maps as shown in Equation (2):  
\[W_{\gamma}=\{w_{i}\}=\frac{\gamma_{i}}{\sum_{j=1}^{C}\gamma_{j}},\;i,j=1,2, \ldots,C \tag{2}\]  
The weights of the \(W_{\gamma}\)-weighted feature mapping are mapped to the range (0, 1) using the _Sigmoid_ function. Next, a threshold is set (in the experiment, the threshold is set to 0.5), and the weights above the threshold are set to 1 to obtain the informative weights \(W_{1}\). At the same time, the weights below the threshold \(W_{2}\) are set to 0 to obtain the non-informative weights. The whole process of obtaining \(W\) can be expressed as Equation (3):  
\[W=Gate\;(Sigmoid\;(W_{\gamma}(GN\;(X)))) \tag{3}\]  
Finally, the input features \(X\) are multiplied by \(W_{1}\) and \(W_{2}\) to obtain two different sets of weighted features: information-rich features \(X_{1}^{w}\) and information-poor features \(X_{2}^{w}\). Figure 3: SRU structure diagram. To reduce the spatial redundancy of features, further reconstruction operations are performed. The interaction between different channels (channels with more information and channels with less information) is identified to enhance the flow of information between these channels as a way to improve accuracy, reduce redundant features, and enhance the feature representation capability of the CNN. Adding information-rich features with information-poor features to generate information-rich features saves spatial resources. The method of cross-reconstruction, instead of simple direct addition, is used, which can more fully integrate two weighted different information features and enhance the information exchange between them. Then, the cross-reconstructed features \(X^{w1}\) and \(X^{w2}\) are fused and spliced together using element multiplication, element summation, and splicing operations to obtain the spatially refined feature map \(X^{w}\). The whole reconstruction process can be expressed as Equation (4):  
\[\left\{ \begin{array}{l}X_{1}^{w}=W_{1}\otimes X,\\ X_{2}^{w}=W_{2}\otimes X,\\ X_{11}^{w}\oplus X_{22}^{w}=X^{w1},\\ X_{21}^{w}\oplus X_{12}^{w}=X^{w2},\\ X^{w1}\cup X^{w2}=X^{w}.\end{array} \right.