## 2 Materials and Methods
### Cross-Channel Spatial Attention Encoder  
To model and integrate multi-scale information from feature extractors more accurately, a Transformer-based method is designed, which includes three token encoders and three token decoders to capture and aggregate contextual information from features of different scales. The primary task of the token encoders is to encode the global contextual information of features through cross-scale spatial attention modules and Transformer modules. First, considering computational and storage resource constraints, the spatial attention module transforms the input features into three-dimensional token embeddings of the target size. These embeddings are then passed to the Transformer module for further extraction and integration of feature information. Through this design, multi-scale contextual information is effectively captured and integrated, enhancing the model's feature representation ability and generalization performance. The specific steps are as follows: given input features \(F\in R^{b\times c\times h\times w}\), cross-channel attention is first processed through the Efficient Channel Attention (ECA) module. The processing steps of the ECA module are as follows: Global average pooling is performed on \(F\), resulting in global features \(F_{avg}\in R^{b\times c\times 1\times 1}\). A 1D convolution operation is then applied to \(F_{avg}\), yielding cross-channel weights \(W\in R^{b\times c\times 1\times 1}\), where the kernel size k is automatically determined by the ECA module based on the number of channels. The weights \(W\) are applied to the original input features \(F\), resulting in enhanced features \(F_{ECA}\), represented as \(F_{ECA}=F\cdot W\), which denotes element-wise multiplication. A \(1\times 1\) convolution layer is used to convert \(F_{ECA}\) into an intermediate feature, denoted as \(F^{\prime}\in R^{b\times l\times h\times w}\). The enhanced features \(F_{ECA}\) and \(F^{\prime}\) are then reshaped into three-dimensional tokens, denoted as \(F_{1}\in R^{b\times c\times(h\times w)}\) and \(F_{2}\in R^{b\times c\times(h\times w)}\). Finally, \(F_{1}\) and \(F_{2}\) are merged into a token \(t\in R^{b\times l\times c}\), represented as  
\[t_{b\times l\times c}=F_{1b(lkw)}F_{2 bc(lkw)} \tag{4}\]  
where b, c, h, and w denote the batch size, number of channels, height, and width of the input features F, respectively; 1 is the token length, set to 4 in the model. To model the contextual information in the tokens, a Transformer encoder is used. First, a set of trainable parameters for positional embedding (PE) is added to the token t. The Transformer encoder employs a standard structure, including multi-head self-attention (MHA) blocks and feed-forward neural network (FFN) blocks, with layer normalization (LN) applied before each block. The MHA initially expands t through a linear layer into a new embedding, denoted as \(t^{\prime}=tW_{I}\), where \(t^{\prime}\in R^{b\times l\times(n\times d\times 3)}\), with \(W_{I}\) representing the weights of the linear layer, n representing the number of MHA heads, and d representing the dimension of each head. Here, n and d are set to 8 and 64, respectively. The embedding \(t^{\prime}\) is then input into the different heads of the MHA, with parameters not shared between heads. Each head consists of two steps: linear transformation and scaled dot-product attention (SDPA). \(t^{\prime}\) is mapped into queries \(Q\in R^{b\times l\times(n\times d\times 3)}\), \(K\in R^{b\times l\times(n\times d\times 3)}\), and \(V\in R^{b\times l\times(n\times d\times 3)}\), represented as  
\[Q,K,V=t^{\prime}W_{Q},t^{\prime}W_{K},t^{\prime}W_{V} \tag{5}\]  
where \(W_{Q}\), \(W_{K}\), and \(W_{V}\) represent the weights of the linear layers mapping \(Q\), \(K\), and \(V\), respectively.