There is a key problem in the medical visual question answering task that how
to effectively realize the feature fusion of language and medical images with
limited datasets. In order to better utilize multi-scale information of medical
images, previous methods directly embed the multi-stage visual feature maps as
tokens of same size respectively and fuse them with text representation.
However, this will cause the confusion of visual features at different stages.
To this end, we propose a simple but powerful multi-stage feature fusion
method, MF2-MVQA, which stage-wise fuses multi-level visual features with
textual semantics. MF2-MVQA achieves the State-Of-The-Art performance on
VQA-Med 2019 and VQA-RAD dataset. The results of visualization also verify that
our model outperforms previous work.

本研究提出了一种简单却强大的多阶段特征融合方法 MF2-MVQA，它能有效利用医学图像的多尺度信息，实现了医疗视觉问答任务中语言与医学图像的特征融合，并在 VQA-Med 2019 和 VQA-RAD 数据集上实现了最先进的性能，可视化结果也验证了我们的模型胜过以前的工作。