## 3 Results
### Single-Source vs. Multi-Source Training  
We wanted to investigate whether the use of multi-source data allows trained models to have a better performance and higher generalization power in comparison with training with single-source data. Therefore, in addition to the model trained on multi-source data, we trained another set of models using the same architecture and training pipeline but using only data from each training dataset individually. The results from the training are shown in Tables 6-11. Each table shows a matrix of train and test datasets (x and y axes) with the values of a specific metric, so that each metric corresponds to the model trained on the specified training dataset and evaluated on the specified test dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**AP** & **CheXpert** & **MIMIC-CXR** & **NIH** & **PadChest** & **SIM-ACR** & **VinDR-CXR** & **Multi-Source (All)** \\ \hline \hline \multirow{2}{*}{BRAX} & **0.211** & 0.2 & 0.173 & 0.065 & 0.05 & 0.008 & 0.199 \\  & (0.148–0.278) & (0.139–0.265) & (0.117–0.233) & (0.036–0.101) & (0.033–0.073) & (0.006–0.009) & (0.137–0.264) \\ \hline \multirow{2}{*}{CANDID-PTX} & 0.841 & 0.829 & 0.753 & 0.619 & 0.645 & 0.424 & **0.892** \\  & (0.83–0.852) & (0.817–0.84) & (0.74–0.766) & (0.601–0.637) & (0.628–0.663) & (0.407–0.442) & (0.883–0.9) \\ \hline \multirow{2}{*}{CRADI} & 0.361 & 0.615 & 0.542 & 0.287 & 0.514 & 0.122 & **0.726** \\  & (0.292–0.433) & (0.544–0.682) & (0.471–0.615) & (0.215–0.353) & (0.444–0.583) & (0.094–0.156) & (0.661–0.789) \\ \hline \multirow{2}{*}{Indiana} & 0.356 & 0.346 & 0.273 & 0.114 & 0.085 & 0.07 & **0.536** \\  & (0.186–0.531) & (0.174–0.514) & (0.122–0.422) & (0.035–0.23) & (0.039–0.148) & (0.026–0.143) & (0.344–0.704) \\ \hline \multirow{2}{*}{Micro-Average} & 0.442 & 0.497 & 0.435 & 0.271 & 0.324 & 0.156 & **0.588** \\  & (0.167–0.717) & (0.218–0.777) & (0.172–0.698) & (0.021–0.522) & (0.023–0.624) & (0.029–0.341) & (0.291–0.886) \\ \hline \multirow{2}{*}{**Overall**} & 0.741 & 0.724 & 0.679 & 0.44 & 0.569 & 0.218 & **0.825** \\  & (0.728–0.753) & (0.711–0.737) & (0.667–0.692) & (0.425–0.455) & (0.552–0.585) & (0.208–0.227) & (0.814–0.834) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average precision (PR–AUC) results for external test sets using single-source and multi-source training datasets. Best results in bold. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ROC-AUC** & **CheXpert** & **MIMIC-CXR** & **NIH** & **PadChest** & **SIM-ACR** & **VinDR-CXR** & **Multi-Source (All)** \\ \hline \hline \multirow{2}{*}{BRAX} & 0.764 & 0.796 & **0.829** & 0.704 & 0.769 & 0.483 & 0.828 \\  & (0.722–0.809) & (0.757–0.836) & (0.794–0.864) & (0.658–0.75) & (0.728–0.809) & (0.438–0.526) & (0.792–0.865) \\ \hline \multirow{2}{*}{CANDID-PTX} & 0.945 & 0.937 & 0.901 & 0.839 & 0.863 & 0.758 & **0.962** \\  & (0.94–0.949) & (0.932–0.942) & (0.894–0.907) & (0.83–0.847) & (0.856–0.87) & (0.749–0.767) & (0.958–0.966) \\ \hline \multirow{2}{*}{CRADI} & 0.914 & 0.954 & 0.924 & 0.849 & 0.886 & 0.831 & **0.969** \\  & (0.889–0.936) & (0.935–0.97) & (0.901–0.944) & (0.818–0.875) & (0.856–0.915) & (0.803–0.858) & (0.952–0.982) \\ \hline \multirow{2}{*}{Indiana} & **0.934** & 0.9 & 0.847 & 0.748 & 0.841 & 0.851 & 0.896 \\  & (0.852–0.983) & (0.834–0.952) & (0.751–0.927) & (0.626–0.836) & (0.743–0.92) & (0.787–0.906) & (0.809–0.965) \\ \hline \multirow{2}{*}{Micro-Average} & 0.889 & 0.897 & 0.875 & 0.785 & 0.840 & 0.731 & **0.914** \\  & (0.805–0.974) & (0.826–0.968) & (0.831–0.920) & (0.714–0.856) & (0.789–0.890) & (0.561–0.901) & (0.848–0.980) \\ \hline \multirow{2}{*}{Overall} & 0.941 & 0.926 & 0.916 & 0.828 & 0.895 & 0.783 & **0.961** \\  & (0.937–0.945) & (0.921–0.931) & (0.91–0.921) & (0.822–0.835) & (0.89–0.9) & (0.776–0.79) & (0.958–0.965) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Area under the ROC curve (ROC–AUC) results for external test sets using single-source and multi-source training datasets. Best results in bold.