## 2 Related Work  
### Shortcut and Skip Connections  
Since the introduction of ResNet [15], the design of residuals has profoundly influenced the construction of deep neural networks. The mapping relationship it achieves is shown in the Equation (1), where \(x\) is the input and \(f(x)\) is the output after passing through a layer. This design allows the input to propagate forward more efficiently through cross-layer data pathways. In expanding ResNet, DenseNet made a slight adjustment to the residuals by replacing ResNet's simple addition with concatenation [22]. Consequently, after applying an increasingly complex sequence of functions, the mapping of \(x\) to its expansion follows the Equation (2) shown. Incorporating the expansion into a multi-layer perceptron further reduces the number of features.  
\[\vec{x}\to f(\vec{x})+\vec{x} \tag{1}\]  
\[\vec{x}\rightarrow\begin{bmatrix}\vec{x}\\ f_{1}(\vec{x})\\ f_{2}([\vec{x},f_{1}(\vec{x})])\\ f_{3}([\vec{x},f_{1}(\vec{x}),f_{2}([\vec{x},f_{1}(\vec{x})])])\end{bmatrix} \tag{2}\]  
In downstream image classification tasks, U-Net has shown impressive results in medical image segmentation. U-Net also introduces a skip connection mechanism [13], allowing the encoder and decoder at the same depth to use skip connections when passing feature maps rather than simply downsampling and then upsampling. This design partially mitigates the problem of feature loss during downsampling. Building on this, U-Net++ introduced additional skip connections [14] similar to DenseNet, further reducing feature loss.  
However, the major limitation of previous studies, whether U-Net or U-Net++, is that they restore features only at the same layer level, overlooking feature loss during downsampling, which is the most pronounced limitationand one of the motivations for proposing ResNet. In the encoder-decoder structure of U-Net, the issue of feature loss within the encoder has not been effectively addressed.  
To tackle this, we propose a novel skip connection mechanism based on the encoder-decoder architecture. The main concept of this mechanism is to introduce skip connections within the encoder, beyond the conventional feature transmission process. Using cross-layer data pathways, shallow-layer features are directly passed to the deeper network while retaining the shallow-layer pathways that transmit features to the decoder. Visually, this design is asymmetrical compared to U-Net's skip connections; hence, we call it the Asymmetric Skip Connection (ASC). This mechanism is superior to previous skip connections, as it provides data pathways not only from the encoder to the decoder but also cross-layer pathways within the encoder itself. Through these pathways, input feature maps can propagate downward more quickly while retaining their original characteristics. In terms of feature extraction, this mechanism performs excellently. As illustrated in Figure 1, this asymmetric encoder-decoder network design facilitates the fusion of multi-level features, significantly enhancing the model's capacity to mitigate feature vanishing and improve feature representation. Through the ASC, we effectively address the issue of feature loss during downsampling.