## 2 Proposed Method
### Transfer Learning and Fine-Tuning Strategy  
This section describes the notation and definitions about transfer leaning. The necessary notation and definitions refer to [PERSON]'s Survey of Transfer Learning [35]. First, domain \(\mathcal{D}\) and task \(\mathcal{T}\) are defined. Domain \(\mathcal{D}\) consists of the collected feature space \(\mathcal{X}\) and its marginal probability distribution \(P(X)\), where \(X=\{x_{1},\cdots,x_{n}\}\in\mathcal{X}\), and \(n\) is the number of instances in each domain (\(\mathcal{D}=\{\mathcal{X},\ P(X)\}\). The symbol \(X\) denotes an instance set, which is specific training sample, and \(x_{i}\) is the \(i^{th}\) instance in particular domain. Task \(\mathcal{T}\) consists of label space \(\mathcal{Y}\) and prediction model \(f(\cdot)\) (\(\mathcal{T}=\{\mathcal{Y},\hat{f}(\cdot)\}\)). The symbol \(Y=\{y_{1},\cdots,y_{n}\}\in\mathcal{Y}\) denotes the label set corresponding to an instance set \(X\), and \(y_{i}\) is a label corresponding to the instance \(x_{i}\). Prediction model \(f(\cdot)\) is not observed and collected but is trained using collected training data. In this paper, the trained prediction model \(f(\cdot)\) predicts the corresponding health state label \(f(x)\), of an input instance \(x\). In terms of probabilistic method, \(f(x)\) can be denoted as \(P(y|x)\). In transfer learning, two types of domains exist: source domain and target domain. The target domain is the domain to proceed with the diagnosis task, whereas the source domain is a domain different but related to the target domain, which has useful information for the target domain. Transfer learning can provide useful knowledge in \(\mathcal{D}_{S}\) and \(\mathcal{T}_{S}\) to improve the performance of target prediction model \(f_{T}(\cdot)\), where \(\mathcal{D}_{S}\
eq\mathcal{D}_{T}\) or \(\mathcal{T}_{S}\
eq\mathcal{T}_{T}\). Thus, a relation that is visibly obvious or inherent must exist between the source and target domains. When this condition is satisfied, the two domains are related. If the source domain is not related to the target domain, the information held by the source domain does not improve the performance of the target prediction model; instead, it causes a negative transfer that reduces performance [26]. We denote source domain data as \(D_{S}=\Big{\{}(x_{S_{1}},y_{S_{1}}),\ldots,\Big{(}x_{S_{n_{S}}},y_{S_{n_{S}}} \Big{)}\Big{\}}\), where \(x_{S_{i}}\) and \(y_{S_{i}}\) are \(i^{th}\) source instance and class label, respectively. \(n_{S}\) is the number of instances in the source domain. Source domain data \(D_{S}\) are employed for training a pre-trained model. In \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Type** & **Data Dimension** \\ \hline \multirow{4}{*}{Conv1} & Input & \#,224,224,3 \\  & ConvolutionalLayer & \#,112,112,64 \\  & batchNormalizationLayer & \#,112,112,64 \\  & conv1\_relu & Ramp & \#,112,112,64 \\  & pool1\_pad & PaddingLayer & \#,113,113,64 \\  & pool1 & PoolingLayer & \#,56,56,64 \\  & 2a & ResidualBlock2 (12 nodes) & \#,56,56,256 \\  & 2b & ResidualBlock2 (10 nodes) & \#,56,56,256 \\  & 2c & ResidualBlock2 (10 nodes) & \#,56,56,256 \\  & 3a & ResidualBlock3 (12 nodes) & \#,28,28,512 \\  & 3b & ResidualBlock3 (10 nodes) & \#,28,28,512 \\  & 3c & ResidualBlock3 (10 nodes) & \#,28,28,512 \\  & 3d & ResidualBlock3 (10 nodes) & \#,28,28,512 \\  & 4a & ResidualBlock4 (12 nodes) & \#,14,14,1024 \\  & 4b & ResidualBlock4 (10 nodes) & \#,14,14,1024 \\  & 4c & ResidualBlock4 (10 nodes) & \#,14,14,1024 \\  & 4d & ResidualBlock4 (10 nodes) & \#,14,14,1024 \\  & 4e & ResidualBlock4 (10 nodes) & \#,14,14,1024 \\  & 4f & ResidualBlock4 (10 nodes) & \#,14,14,1024 \\  & 5a & ResidualBlock5 (12 nodes) & \#,7,7,2048 \\  & 5b & ResidualBlock5 (10 nodes) & \#,7,7,2048 \\  & 5c & ResidualBlock5 (10 nodes) & \#,7,7,2048 \\  & pool5 & PoolingLayer & \#,1,1,2048 \\  & flatten\_0 & FlattenLayer & \#,2048 \\  & Fc2 & LinearLayer & \#,2 \\  & prob & SoftmaxLayer & \#,2 \\  & Output & class \\ \hline \hline \end{tabular}
\end{table}