# Improving Medical X-ray Report Generation by Using Knowledge Graph
## 4 Experiments
### Qualitative Results  
In Figure 3, we visualize the radiology reports of the three sets of images. The same color in the reference report and the generated report indicates the same description of the chest X-ray image, and the underscore indicates additional findings of our model for the chest X-ray image. In the first example, the reports generated by our model are almost  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Acc.**} & \multicolumn{6}{c}{**Macro Scores**} & \multicolumn{3}{c}{**Micro Scores**} \\ \cline{3-10}  & & & **AUC.** & **F-1.** & **Prec.** & **Rec** & **AUC.** & **F-1.** & **Prec.** & **Rec** \\ \hline \multirow{6}{*}{Open-I} & S\&T [11] & 0.915 & N/A & N/A & N/A & N/A & N/A & N/A & N/A \\  & SA\&T [16] & 0.908 & N/A & N/A & N/A & N/A & N/A & N/A & N/A \\  & TieNet [21] & 0.902 & N/A & N/A & N/A & N/A & N/A & N/A & N/A & N/A \\  & [PERSON] et al. [24] & 0.918 & N/A & N/A & N/A & 0.190 & N/A & N/A & N/A & N/A & N/A \\  & [PERSON] et al. [26] & 0.937 & 0.702 & 0.152 & 0.142 & 0.173 & 0.877 & 0.626 & 0.604 & 0.649 \\  & Ours & **0.938** & **0.749** & **0.193** & **0.246** & **0.181** & **0.925** & **0.636** & **0.614** & **0.660** \\ \hline \multirow{6}{*}{MIMIC-CXR} & SA\&T [16] & N/A & N/A & 0.101 & 0.247 & 0.119 & N/A & 0.282 & 0.364 & 0.230 \\  & AdpAtt [17] & N/A & N/A & 0.163 & 0.341 & 0.166 & N/A & 0.347 & 0.417 & 0.298 \\ \cline{1-1}  & [PERSON] et. al [24] & 0.867 & N/A & N/A & 0.309 & 0.134 & N/A & N/A & **0.586** & 0.237 \\ \cline{1-1}  & GumbelTransformer [39] & N/A & N/A & 0.214 & 0.327 & 0.204 & N/A & 0.398 & 0.461 & 0.350 \\ \cline{1-1}  & [PERSON] et al. [26] & 0.887 & 0.784 & 0.412 & 0.432 & 0.418 & 0.874 & 0.576 & 0.567 & 0.585 \\ \cline{1-1}  & Ours & **0.890** & **0.858** & **0.560** & **0.587** & **0.593** & **0.907** & **0.640** & 0.579 & **0.715** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The clinical accuracy of the generated reports was quantitatively compared by evaluating 14 common diseases defined together in the CheXpert and MIMIC-CXR datasets, with the best results highlighted using bold font.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **Methods** & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** & **METEOR** & **ROUGE-L** \\ \hline \multirow{6}{*}{Open-I} & Base with \(D_{enriched}\) & 0.445 & 0.328 & 0.256 & 0.206 & 0.198 & 0.434 \\  & Base with \(D_{enriched}\) + & & & & & & \\  & Interpreter & & & & & & \\  & Base with \(D_{pri\text{-}enriched}\) & 0.475 & 0.349 & 0.273 & 0.222 & 0.208 & 0.433 \\  & Base with \(D_{pri\text{-}enriched}\) & **0.505** & **0.379** & **0.302** & **0.251** & **0.218** & **0.446** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation studies. Base with Denriched refers to the model composed of the classifier and generator mentioned in this paper, and the classifier uses enriched disease embedding. Base with \(D_{pri\text{-}enriched}\) refers to the model composed of the classifier and generator mentioned in this paper. Rich disease embedding with prior knowledge is used in the classifier. Interpreter is the Interpreter mentioned above. The best results are highlighted in bold.  
identical to the actual situation. In the second example, the report generated by our model completely covers the actual situation and finds an additional disease based on the input image. Although this disease may indeed not exist, it can be used as a reminder to assist the doctor to make a more comprehensive diagnosis. In the third example, our model generates a longer report based on the obtained features, and it is almost the same as the actual situation, indicating that our model also has a relatively good performance in generating complex sentences. It can be seen that our method can extract features from images and clinical documents combined with prior knowledge to generate readable and accurate reports.