Medical visual question answering with symmetric interaction attention and cross-modal gating

The purpose of medical visual question answering (Med-VQA) is to provide accurate answers to clinical questions related to visual content of medical images. However, previous attempts neglect to take full advantage of the information interaction between medical images and clinical questions, which hinders the further progress of Med-VQA. The above issue requires the efforts to focus on critical information interaction within each modality and relevant information interaction between modalities. In this paper, we utilize the multiple meta-model quantifying model as visual encoder and the GloVe word embedding followed by the LSTM as textual encoder to form our feature extraction module. Then, we design a symmetric interaction attention module to construct dense and deep intra- and inter-modal information interaction on medical images and clinical questions for the Med-VQA task. Specifically, the symmetric interaction attention module consists of multiple symmetric interaction attention blocks that contain two basic units, i.e., self-attention and interaction attention. Technically, self-attention is introduced for intra-modal information interaction, while interaction attention is constructed for inter-modal information interaction. In addition, we develop a multi-modal fusion scheme that leverages the cross-modal gating to effectively fuse multi-modal information and avoid redundant information after sufficient intra- and inter-modal information interaction. Experimental results on the VQA-RAD dataset and PathVQA dataset show that our method outperforms other state-of-the-art Med-VQA models, achieving 74.7% and 48.7% on accuracy, 73.5% and 46.0% on F1-score, respectively.