## 3 Proposed Method
### Local Structure Module  
In traditional settings, UDA methods are based on the feature alignment strategy of source and target features. But in the SFA setting, it is not feasible to access the source data. We usually obtain a class prediction \(p(x)\) and feature embedding space \(f(x)\). Following [9], the local structure method intends to shift the target features with the source domain. In the first place, due to the expected domain shift, some of the target features can be deviated by far from the source feature region. But in this case, we still consider that feature space retains the clusters that are formed by the classes. For this, we actually measure the distance and move the data point into the close likely data point cluster. Due to the domain shift between the source and target interpatient data, there can be wrong predictions by the classifier. So, it is essential to cluster target features that are in the same class clustered together. Therefore, it is obvious that the nearest neighbor target features tend to have share category labels. As a result, the clustered features are most likely to have an inclination toward a common label jointly. As shown in Figure 2, this scheme helps to classify target features correctly that possibly have the wrong classification. To ensure semantically close neighborhoods, we use a feature extractor bank \(\mathcal{F}=\{(f(x_{i}))_{\epsilon\mathcal{D}_{i}}\) to accumulate joint target features. Then, we utilize a neighborhood selection procedure to ensure the best clusters among target features. It explicitly encourages correct classification. We take a few nearest neighbors from the feature extractors with a consistency regularization. To store the softmax predicted scores, we use a score bank \(\mathcal{S}=\{g(f(x_{i}))_{\epsilon\mathcal{D}_{i}}\). Here, \(g,f(x_{i})\) are the classifier and feature extractor, respectively. To achieve this, we adopt the ResNet-50 model with a fully connected layer as the feature extractor \(f(x_{i})\), and an additional fully connected layer as classifier \(g\). The local clustering is obtained by adopting the following loss in Equation (2) for consistent predictions in the k-nearest features [9]. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Discriminator** & & & **Generator** & \\ \hline
**Layers** & **Output Shape** & **Parameters** & **Layers** & **Output Shape** & **Parameters** \\ \hline Conv1D & (None, 180, 8) & 72 & Reshape & (None, 100, 1) & 0 \\ \hline LeakyReLU & (None, 180, 8) & 0 & Bidirectional & (None, 100, 32) & 2304 \\ \hline Dropout & (None, 180, 8) & 0 & Conv1D & (None, 100, 32) & 8224 \\ \hline MaxPooling1D & (None, 60, 8) & 0 & LeakyReLU & (None, 100, 32) & 0 \\ \hline Conv1D & (None, 60, 16) & 1040 & UpSampling1D & (None, 200, 32) & 0 \\ \hline Dropout & (None, 60, 16) & 0 & Conv1D & (None, 200, 16) & 4112 \\ \hline MaxPooling1D & (None, 29, 16) & 0 & LeakyReLU & (None, 200, 16) & 0 \\ \hline Conv1D & (None, 15, 32) & 4128 & UpSampling & (None, 200, 16) & 0 \\ \hline LeakyReLU & (None, 15, 32) & 0 & Conv1D & (None, 200, 16) & 1032 \\ \hline Dropout & (None, 15, 32) & 0 & LeakyReLU & (None, 400, 8) & 0 \\ \hline MaxPooling1D & (None, 7, 32) & 0 & Conv1D & (None, 400, 1) & 65 \\ \hline Conv1D & (None, 4, 64) & 16,448 & Flatten & (None, 400) & 0 \\ \hline LeakyReLU & (None, 4, 64) & 0 & Dense & (None, 180) & 72,180 \\ \hline Dropout & (None, 4, 64) & 0 & Activation & (None, 180) & 0 \\ \hline MaxPooling1D & (None, 64) & 0 & Reshape & (None, 180, 1) & 0 \\ \hline Flatten & (None, 64) & 0 & - & - & - \\ \hline Dense & (None, 1) & 65 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model description of GAN framework for data adjustment. \[