## 3 Method
### Multi-Scale Sampling Decoder  
The multi-Scale Sampling Decoder (SSD) is analogous to the Heterogeneous encoder and comprises four stages, each employing bilinear interpolation for upsampling between stages. The structure of the multi-scale adoption decoder is illustrated in Figure 8A. SSD consists of two distinct components. In the initial stage, the output of MaxViT is input into Decoder 1 for decoding purposes. Subsequently, the feature map generated by Decoder 1 is fed into the OC-S module, which weights the feature map that is about to be input into CSWin. In the second part, the output of CSWin is input into Decoder 2. Decoder 2 accepts the outputs from CSWin, MaxViT, and Decoder 1, and fuses these three feature maps during the decoding stage. The fused feature map is then output to the classification header. The formula representation of SSD is shown in Equation (6). \[ \begin{cases}Set_{y_{i}}^{M}=Decoder1\left(Set_{x_{i}}^{M}\right)+Set_{y_{i}}^ {M},i\in[1,4]\\ Set_{y_{i}}^{C}=Decoder2\left(Set_{x_{i}^{\prime}}^{C},Set_{y_{i}}^{M}\right)+ Set_{x_{i}^{\prime}}^{C},i\in[1,4]\end{cases}  \tag{6}\]  
In this context, \(Set\) denotes a collection of feature maps, \(x_{i}^{M}\) represents a feature map of the \(i\)th stage output by MaxViT, \(y_{i}^{M}\) represents a feature map of the \(i\)th stage output by Decoder 1, \(x_{i}^{C}\) represents a feature map of the \(i\)th stage output by CSWin, and \(y_{i}^{C}\) represents a feature map of the \(i\)th stage output by Decoder 2. Decoder1 accepts as input the feature map output by MaxViT, which is expressed as shown in Equation (7). \[ \begin{split} x_{i}^{M^{*}}=Decoder1_{i}\left(x_{i-1}^{M^{*}} \right)+x_{i}^{M^{*}},i\in[1,4]\\ y_{d1}=OC-S\left(x_{4}^{M^{*}}\right)\end{split}  \tag{7}\]  
\(Decoder1_{i}\) denotes the Decoder1 block of in \(i\) stage, \(x_{i}^{M}\) denotes the feature map output by MaxViT of the current stage, \(x_{i-1}^{M^{*}}\) denotes the feature map output by Decoder1\({}_{i-1}\), \(x_{i}^{M^{*}}\) represents the feature map of the current stage output by Decoder 1, and the feature map \(x_{4}^{M^{*}}\) generated by the final stage of Decoder 1 enters the \(OC-S\) to generate the weighted map \(y_{d1}\). Decoder 2 accepts as input the feature map output by Decoder 1 with the feature map output by CSWin, which is expressed as shown in Equation (8). \[x_{i}^{C^{*}}=Decoder2_{i}\left(x_{i-1}^{C^{*}}\right)+x_{i}^{M^{*}}x_{i}^{C},i\in[1,4] \tag{8}\]  
where \(Decoder2_{i}\) denotes the Decoder 2 block of the \(i\)th stage, \(x_{i}^{C}\) denotes the feature map output by CSWin for the current stage, \(x_{i-1}^{C^{*}}\) denotes the feature map output by Decoder 2 for the current stage, and \(x_{i}^{M^{*}}\) denotes the feature map output by Decoder 1 for the \(i\)th stage. \(OC-S\) is responsible for processing the MaxViT-generated feature map \(x\) into a weighted map to weight the coding work for CSWin. \(OC-S\) represented by the formula as shown in Equation (9). \[y=MaxPooling(LN(x)) \tag{9}\]  
where \(y\) represents the weighted map, \(x\) represents the feature map of the input \(OC-S\), \(MaxPooling\) represents the maximum pooling operation, and \(LN\) represents the layer normalization. Decoder 1 and Decoder 2 have the same internal structure for their decoder blocks; the internal structure of the decoder block is shown in Figure 8B. In this structure, the feature map is first weighted by channel attention [44], followed by spatial attention [45], and finally enters the sequence convolution aggregation module to extract multi-scale organ features. The formula representation of the decoder block is shown as follows:\[ \begin{split} x^{{}^{\prime}}=CA(x)\odot x\\ x^{{}^{\prime\prime}}=SA\Big{(}x^{{}^{\prime}}\Big{)}\odot x^{{}^{ \prime}}\\ y=SCAM\Big{(}x^{{}^{\prime\prime}}\Big{)}\end{split}