  Medical visual question answering (VQA) aims to answer clinically relevant
questions regarding input medical images. This technique has the potential to
improve the efficiency of medical professionals while relieving the burden on
the public health system, particularly in resource-poor countries. Existing
medical VQA methods tend to encode medical images and learn the correspondence
between visual features and questions without exploiting the spatial, semantic,
or medical knowledge behind them. This is partially because of the small size
of the current medical VQA dataset, which often includes simple questions.
Therefore, we first collected a comprehensive and large-scale medical VQA
dataset, focusing on chest X-ray images. The questions involved detailed
relationships, such as disease names, locations, levels, and types in our
dataset. Based on this dataset, we also propose a novel baseline method by
constructing three different relationship graphs: spatial relationship,
semantic relationship, and implicit relationship graphs on the image regions,
questions, and semantic labels. The answer and graph reasoning paths are
learned for different questions.
