# Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion
## 4 Experiments
### Experimental Results  
Table 2 shows the comparison results of our proposed method on the Rad-ReStruct dataset with the baseline model, Hi-VQA, and the most advanced context-VQA method. For fair comparison, we adopted the same evaluation method for Hi-VQA and context-VQA, as introduced above, and used the indicators of accuracy, F1 value, precision and recall to evaluate our results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Acc** \\ \hline MEVF [43] & 66.1 \\ MMQ [44] & 67.0 \\ MM-BERT [45] & 72.0 \\ CRPD [46] & 72.7 \\ RepSNet [47] & 73.5 \\ MSAE [48] & 77.0 \\ Hi-VQA [9] & 76.3 \\ HiCA-VQA (Ours) & 79.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative Experiments on Previous General-Purpose Datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Report Accuracy** & **F1** & **Prec** & **Recall** \\ \hline Hi-VQA [9] & 32.6 & 31.9 & 59.9 & 34.1 \\ con-VQA [29] & 39.7 & 31.0 & **90.4** & 33.6 \\ HiCA-VQA (Ours) & **39.9** & **49.1** & 69.8 & **34.2** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison on Rad-Restruct dataset. We compared three methods in total: Hi-VQA, context-VQA and our HiCA-VQA, where the best scores are **in bold**.  
In terms of report-level accuracy, HiCA-VQA, a hierarchical cueing and cross-attention visual question answering, achieved the highest metric, improving by about 20 percent compared to Hi-VQA and surpassing the SOTA context-VQA, which reflects our improved ability to fully predict reports. At the same time, our F1 score is 18% higher than other methods.  
Table 3 shows the indicators of Hi-VQA, context-VQA and HiCA-VQA at each question level. It can be seen that HiCA-VQA has improved F1 at each level, and the F1 score of the third-level fine-grained complex questions has increased by more than 20 percent. This hierarchical performance gain fully verifies the effectiveness of the hierarchical prompt mechanism and cross-modal cross-attention module proposed in this paper. The hierarchical decoding strategy guides the model to gradually focus on visual semantic features of different granularities, and the attention weight allocation mechanism based on region alignment better enhances the model's collaborative perception of multi-modal fine-grained features, especially when dealing with complex reasoning tasks that require comprehensive image spatial features and text semantic constraints. It shows stronger feature decoupling and fusion capabilities. Prove the effectiveness of our method in complex medical image question answering. These results prove that hierarchical prompts can make the model perform better in learning deeper and more complex problems. Report accuracy can be understood as the path accuracy in a decision tree. If all three questions along a given path are predicted correctly, the path is considered correct; otherwise, it is considered incorrect. We use path accuracy instead of regular accuracy to encourage the model to make correct predictions along an entire diagnostic path. From the results above, we can see that our model achieves competitive performance in terms of report accuracy, though the improvement is not substantial. However, there is a significant improvement in the F1 score. Despite the large scale of the Rad-Restruct dataset, which includes 3720 images matching 3597 structured patient reports and involves over 180,000 questions, the dataset may have relatively fewer positive samples, especially for level 3 questions. This is because level 3 questions are more complex and have more answer options. As shown by the results above, our method demonstrates a significant improvement in predicting level 3 questions.