## 4 Experimental Results
### 2-DE Images Classification  
Experiments were conducted on actual sepsis classification using 2-DE images (All 2-DE images were generated from actual hospitalized patients and were assessed by doctors using infectious disease tests. This study was approved by the institutional review board, and informed consent was obtained in writing from the patients). Sepsis is a disease caused by a dysregulated host response to infection, which leads to a deadly septic shock and results in many minute protein changes [24]. Using 2-DE images can address this issue because 2-DE images can account for the comprehensive changes in proteins occurring simultaneously.  
Focusing on sepsis data classification as the main task of this paper, we collected the following numbers: sepsis data \(N^{t}(1)=30\) and nonsepsis data \(N^{t}(2)=68\). The performance was evaluated by ten-fold cross-validation. For the source domain, 2-DE images were used with different labels from the target domain sepsis and nonsepsis data. These images were generated from patients who were diagnosed as normal. The source domain task comprised the classification of the differences  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**(A)**} \\ \hline \(\mathbf{N}^{t}\) & **400** & **800** & **1200** & **1500** \\ \hline
[6] & 0.753 & 0.778 & **0.790** & 0.781 \\
[8] & **0.763** & 0.775 & 0.784 & 0.789 \\
[11] & 0.740 & 0.763 & 0.787 & **0.806** \\
[12] & 0.755 & **0.779** & **0.793** & 0.791 \\ \(m_{l}\)[2] & **0.763** & 0.775 & 0.786 & 0.797 \\ Full & 0.724 & 0.752 & 0.750 & 0.753 \\ \(\backslash\)Tuning & 0.750 & 0.738 & 0.735 & 0.734 \\ Ours & **0.779** & **0.791** & **0.793** & **0.799** \\ \hline \multicolumn{5}{c}{**(B)**} \\ \hline \(\mathbf{N}^{t}\) & **1000** & **5000** & **10,000** \\ \hline
[6] & 0.844 & 0.923 & **0.951** \\
[8] & 0.773 & 0.875 & 0.887 \\
[11] & 0.776 & 0.835 & 0.850 \\
[12] & 0.861 & 0.926 & 0.946 \\
[14] & 0.843 & **0.928** & **0.952** \\ \(m_{l}\)[2] & **0.887** & **0.928** & 0.932 \\ Full & 0.854 & 0.926 & 0.945 \\ Tuning & 0.859 & 0.863 & 0.862 \\ Ours & **0.893** & **0.936** & 0.938 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification performances of (**A**) automobile and pedestrian crossings and (**B**) MNIST. The red bold is the best performance of each evaluation. The black bold is the second best performance.  
Figure 3: Results of relation vectors. (**A**) Example of relation vectors for Task (2) estimated by our method. (**B**) The distance between relation vectors of conventional ATDL [2] and our proposed method.  
between the protein extraction and refining protocols [2] (\(N^{s}=180\), \(D_{y}^{s}=9\)), as shown in Table 2. Meanwhile, the target 2-DE images were generated differently from the description in Table 2. They were taken using serum, removing 14 abundant proteins. Figure 4 shows examples of sepsis, nonsepsis, and source domain images. Since the source domain data included many spots, such as minute spot changes, they were expected to also include the information for classifying sepsis.  
In this study, 2-DE images were used as input to the DNN because valid spots for detecting sepsis have not been fully clarified to date. The 2-DE images were downsized to 53 \(\times\) 44 gray-scale pixels due to the limited source and target domain data. This input size was determined in a manner to preserve the information of large spots that were analyzed under the supervision of biologists.