## 3 Methods
### Multi-Scale Feature Extractor  
The retinal vascular structure exhibits a high level of complexity and variability in both size and distribution. A smaller receptive field tends to capture more localized features, making it advantageous for detecting smaller targets. Conversely, features captured by a large receptive field are more global, facilitating the learning of the intricate relationship between the vessel pixels and their surroundings. Therefore, our designed MSFE employs convolutional layers with varying receptive fields to extract features separately, effectively capturing multi-scale information. Given that dilated convolution expands the receptive field without adding more network parameters, we utilize dilated convolution to replace traditional convolution. As illustrated in Figure 3, three parallel convolutional layers with specific dilation rates (1, 2, and 3) are used to independently extract features of different scales. The resulting feature maps are subsequently merged through concatenation.  
As the channel attention module can emphasize crucial information and reduce redundant features by assigning varying weights to individual channels, a Squeeze-and-Excitation (SE) module is incorporated into the MSFE to enhance the feature distinguishing ability. The SE module primarily consists of two steps: \"squeeze\" and \"excitation\". Initially, the feature maps are squeezed using global average pooling, capturing the global distributions of each channel. Subsequently, the feature vector obtained from the squeeze operation is fed into two-layer fully connected layers for excitation. The first layer compresses the dimension to reduce computational complexity, while the second layer expands the dimension back to the original dimension, obtaining the feature map weight. Through excitation operations, the interdependencies between channels can be fully captured. The resulting correction weight is then multiplied with the input feature. Finally, a residual mechanism is introduced to prevent the loss of essential information. The described MSFE is presented in Equations (3)-(7):  
\[f_{m}=Concat(Conv_{3\times 3}^{1}(f_{in}),Conv_{3\times 3}^{2}(f_{in}),Conv_{3 \times 3}^{3}(f_{in})) \tag{3}\]  
\[s=F_{sq}(f_{m})=\frac{1}{w\times h}\sum_{i}^{w}\sum_{j}^{h}f_{m}(i,j) \tag{4}\]  
\[z=F_{ex}(f_{s},W)=\sigma(W_{2}\delta(W_{1}s)) \tag{5}\]  
\[f_{out}=f_{m}\cdot z \tag{6}\]  
\[f_{res\text{-}out}=f_{in}+Conv_{1\times 1}(f_{out}) \tag{7}\]  
Figure 3: The structure of the designed MSFE.  
where Equation (3) represents the dilated convolutional module, with \(Conv_{3\times 3}^{n}\) denoting the dilated convolutional layer with dilation rate \(n\), and \(Concat(\cdot)\) denoting the connection operation. Equations (4)-(6) represent the channel attention module, where \(F_{sq}\) is the global average pooling operation and \(F_{ex}\) is the nonlinear transformation. The width and height of the feature map \(f_{m}\) are denoted by \(w\) and \(h\), respectively; \(W_{1}\) and \(W_{2}\) are adjustable parameters; and \(\sigma\) and \(\delta\) are activation functions. Equation (7) represents the residual mechanism, where \(Conv_{1\times 1}\) is a \(1\times 1\) convolution layer used to adjust the number of channels.