  Research in medical visual question answering (MVQA) can contribute to the
development of computeraided diagnosis. MVQA is a task that aims to predict
accurate and convincing answers based on given medical images and associated
natural language questions. This task requires extracting medical
knowledge-rich feature content and making fine-grained understandings of them.
Therefore, constructing an effective feature extraction and understanding
scheme are keys to modeling. Existing MVQA question extraction schemes mainly
focus on word information, ignoring medical information in the text. Meanwhile,
some visual and textual feature understanding schemes cannot effectively
capture the correlation between regions and keywords for reasonable visual
reasoning. In this study, a dual-attention learning network with word and
sentence embedding (WSDAN) is proposed. We design a module, transformer with
sentence embedding (TSE), to extract a double embedding representation of
questions containing keywords and medical information. A dualattention learning
(DAL) module consisting of self-attention and guided attention is proposed to
model intensive intramodal and intermodal interactions. With multiple DAL
modules (DALs), learning visual and textual co-attention can increase the
granularity of understanding and improve visual reasoning. Experimental results
on the ImageCLEF 2019 VQA-MED (VQA-MED 2019) and VQA-RAD datasets demonstrate
that our proposed method outperforms previous state-of-the-art methods.
According to the ablation studies and Grad-CAM maps, WSDAN can extract rich
textual information and has strong visual reasoning ability.
