## 2 Methodology
### Two-Branch Feature Aggregation Module  
The two-branch feature aggregation module is divided into the two-branch lightweight aggregation module (DLAM) and the dual-branch attention aggregation module (DAAM), which are utilized to blend the features originating from the CNN and Transformer. DLAM is used for the first and second stages of bipolar fusion, and its lightweight design allows it to be applied to shallow features without burden. DLAM is designed to learn the weights of features from the CNN and Transformer. It places more emphasis on the local features of CNN and the global features of Transformer, while de-emphasizing the less significant features. DAAM, a feature reconstruction module leveraging the self-attention mechanism, is used to merge the dual-branch features from the third and fourth stages. By leveraging self-attention, DAAM captures the beneficial feature information from both CNN and Transformer, allowing CNN to gain global insights from Transformer and enabling Transformer to receive local insights from CNN. The detailed structure of DLAM is presented in Figure 4a. Initially, the output features from Stages 1 and 2 of the CNN and Transformer undergo a \(1\times 1\) convolution operation. This operation serves to modify the channel count of the features. Subsequently, the two sets of features are concatenated and then passed through another \(1\times 1\) convolution. This process is beneficial for the interaction between the features from the CNN and the Transformer. Subsequently, the combined features are segmented along the channel dimension through the Split operation. Then, the two separated features are processed, respectively, by a \(1\times 1\) convolution and a Sigmoid function, normalizing them so that their pixel values are within the range of 0\(\sim\)1. This is done to prevent the appearance of extreme maximum or minimum values. Next, the two separated features are connected in parallel along the channel dimension by means of the stack operation. Subsequently,the Softmax function is employed to create a pixel-level mixed-weight map, so that the fusion weight maps of the CNN and the Transformer sum up to 1 at the corresponding pixel positions. In the end, the two generated feature fusion weight maps are multiplied pixel by pixel with the first two input features, reallocating the weights to the features of the CNN and the Transformer, thus enabling a more optimal dual-branch fusion. In addition, we employ residual connections (represented by dashed lines) to incorporate earlier features, which helps speed up model optimization and makes it easier to learn the weights of feature graphs. DAAM is composed of two fundamental attention modules along with two branches for global and local interactions, as illustrated in Figure 4b. The attention module receives inputs from the CNN and Transformer features. Initially, the input \(X\) undergoes a \(1\times 1\) convolution to create a query matrix, while \(Y\) is processed through two \(1\times 1\) convolutions to produce a key matrix and a value matrix, respectively. Next, the query matrix is transposed and multiplied by the key matrix to form an attention map, which is then normalized using the Softmax function. Overall, this process allows the input feature \(y\) to be reweighted by incorporating information from feature \(x\). Therefore, when \(x\) comes from the third or fourth stage Transformer, and \(y\) comes from the third or fourth stage CNN, This process is designed to integrate the global information inherent in the Transformer to fine-tune the local features of the Convolutional Neural Network (CNN), facilitating a seamless transition from the global perspective to the local one.