## 3 Experiments
### Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Abnormality** & **[PERSON] et al. [6]** & **[PERSON] et al. [39]** & **[PERSON] et al. [40]** & **ours-vgg** & **ours-res** \\ \hline atelectasis & 0.716 & 0.719 & 0.733 & 0.734 & **0.788** \\ cardiomegaly & 0.807 & **0.880** & 0.858 & 0.848 & 0.828 \\ effusion & 0.784 & 0.792 & 0.806 & 0.797 & **0.865** \\ infiltration & 0.609 & - & 0.673 & 0.675 & **0.682** \\ mass & 0.706 & **0.809** & 0.777 & 0.793 & 0.766 \\ nodule & 0.671 & 0.711 & 0.718 & 0.708 & **0.732** \\ pneumonia & 0.633 & **0.766** & 0.684 & 0.702 & 0.719 \\ pneumothorax & 0.806 & 0.837 & 0.805 & **0.861** & 0.827 \\ consolidation & 0.708 & 0.734 & 0.711 & **0.792** & 0.724 \\ edema & 0.835 & 0.802 & 0.806 & 0.803 & **0.869** \\ emphysema & 0.815 & 0.841 & 0.842 & 0.839 & **0.853** \\ fibrosis & 0.769 & **0.803** & 0.743 & 0.781 & 0.766 \\ PT & 0.708 & 0.757 & 0.724 & 0.724 & **0.774** \\ hernia & 0.767 & **0.872** & 0.775 & 0.781 & 0.853 \\ A.V.G & 0.738 & 0.789 & 0.761 & 0.772 & **0.791** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Result evaluation and comparison by AUC. chest X-ray image after updates. \"Update order\" is the output of Algorithm 2, which was obtained by mining the subgraphs detected from the association graph Figure 2. A, CA, CO, ED, E, EM, I, M, P, and PX mean atelectasis, cardiomegaly, consolidation, edema, effusion, empysema, infiltration, mass, pneumonia, and pneumothorax, respectively. Figure 3 shows that the ant colony algorithm in Algorithm 2 gradually converges to the shortest path as the number of iterations increases. It can be seen that the shortest path in each subgraph can be found after around 10 iterations. According to the number of nodes in the subgraph, the number of iterations in our experiments were set as 8, 14, and 20, respectively. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Original Order** & A-E-I & A-CO-E & ED-I-P & CO-E-I & ED-E-I & CA-E-I & E-I-M & A-CO-I \\ \hline
**Updated Order** & I-E-A & CO-A-E & I-ED-P & CO-E-I & ED-I-E & I-E-CA & M-E-I & I-A-CO \\ \hline
**Original Order** & A-E-I-PX & A-CO-E-I & A-CA-E-I & A-E-I-M & A-CO-E-M & A-EM-M-PX & & \\ \hline
**Updated Order** & I-E-PX-A & I-A-CO-E & I-A-E-CA & M-E-I-A & A-E-CO-M & A-M-PX-EM & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Some examples of the original label order and updated label order detected as shown in Figure 2 by Algorithm 2. Figure 2: The CLA network built by Algorithm 1. The thickness of an edge implies the correlation intension between two lesions. Figure 4 shows visualization results of eight examples by highlighting the class-specific discriminative regions. In each example, the first image is a chest X-ray image, the text on the top of rest images represents the corresponding predictions of the model. The number below the text represents the confidence level of the model prediction. Most predictions have high confidence, but some predictions have low confidence. For example, although all lesions on the chest radiograph in 2 group are correctly identified, the confidence level for \"effusion\" is only 0.21. Except for the first image, images are superpositions of the gray-scale image and the corresponding chest X-ray image (the first image). The brighter is the gray-scale image, the greater is the contribution of the corresponding area of the chest X-ray image to the prediction result. However, the corresponding active area cannot be clearly seen because the gray-scale images obscure the chest X-ray image under it. Therefore, colored boxes are used to indicate the active location of the predicted lesion. For example, the red box of the second image in 1 group outlines the brightest part of the gray-scale image, which corresponds to the area surrounded by the red box of the first image in 1 group. This area of the chest X-ray image contributes the most to the \"cardiomegaly\" prediction.