## 3 Methods
### Fundus-DANet  
#### 3.1.1 Da-CBAM  
We designed the DA-CBAM for spatial feature learning in images. Without reducing the resolution of the feature maps, this module leveraged three dilated convolution layers and channel and spatial attention mechanisms of the CBAM [6] to focus on significant aspects and acquire multiscale features for learning and fusion. Specifically, Figure 2 depicts the general architecture of the DA-CBAM. First, the channels of feature F were divided into two groups, and the channels within these groups were rearranged to facilitate the exchange and combination of information between the different channels. Then, a dilated convolution layer with a convolution kernel of 3 and expansion rates of 1, 2, and 3 was applied to the channel-shuffled feature F\({}^{\prime}\). The dilated convolution kernel, depicted in Figure 3, incorporates spacing between the filters to expand the sensory field of the network without increasing the number of parameters and computational requirements. Dilated convolutions with different dilation rates can simultaneously capture the local details and global information, enhancing the perception  
Figure 1: The overall structure of Fundus-DANet, DA-CBAM is the spatial feature learning module (green), ILM is the interclass learning module (dark blue), and the asymmetric polynomial loss is represented in pink. Figure 2: The overall architecture of the DA-CBAM. The channels are shuffled between the two groups. Dilated convolutions are then used to extract the features at different scales from each group. These features are processed through hidden layers, and the final results are summed up. of features at various scales. This capability is crucial for handling the fine and diverse characteristics present in images. Given that the image was transformed into a tensor with 1536 channels and a spatial size of \(8\times 8\) after processing through the backbone network, the selection of dilation rates required careful consideration. Excessive dilation rates could exceed the original spatial dimensions and adversely affect the experimental results. Therefore, we constrained the dilation rates to 1, 2, and 3. This ensured an appropriate receptive field while avoiding spatial inconsistencies. On this basis, we obtained three feature vectors, F1, F2, and F3, and the feature maps of these three scales were batch-normalized and then activated by the RELU function as shown in Equations (1) and (2):  
\[Y_{\text{d}}[i,j]=\sum_{0}^{k-1}\sum_{0}^{k-1}X[i+m\times d,j+n\times d]\times W [m,n](k=3,d=1,2,3), \tag{1}\]  
where \(i,j\) denotes the row and column indices of the output feature map \(Y_{\text{d}}\), \(k\) is the size of the dilated convolution kernel, and \(d\) is the dilated convolution rate. \[F_{1},F_{2},F_{3}=\sigma\Big{(}BN\Big{(}Conv(F^{\prime})\Big{)}\Big{)}, \tag{2}\]  
where \(\sigma\) is the activation function, and Conv is the dilated convolution. As seen in Figure 4, each of the three feature maps obtained was subjected to a layer of \(1\times 1\) convolution to reduce the number of channels. The feature maps were then joined at the channel level using the maximum and average pooling, as indicated by Equation (3). This fused the feature maps in the channel and preserved the data from the various feature map categories, enhancing the model's representation and generalization capabilities. This research included the attention mechanism of the CBAM, which determined the significance of each spatial location in the image and improved the focus on the relevant spatial places, helping the network focus on the important aspects of the image. Ultimately, these three features were residual connections and summed up to produce \(F_{1}^{\prime}\), \(F_{2}^{\prime}\), and \(F_{3}^{\prime}\). This process was carried out to improve the network's representation capability and training efficiency by directly passing features from the lower layer to the higher layer and fusing global semantic information with local detail information.