  Medical visual question answering (Med-VQA) has tremendous potential in
healthcare. However, the development of this technology is hindered by the
lacking of publicly-available and high-quality labeled datasets for training
and evaluation. In this paper, we present a large bilingual dataset, SLAKE,
with comprehensive semantic labels annotated by experienced physicians and a
new structural medical knowledge base for Med-VQA. Besides, SLAKE includes
richer modalities and covers more human body parts than the currently available
dataset. We show that SLAKE can be used to facilitate the development and
evaluation of Med-VQA systems. The dataset can be downloaded from
http://www.med-vqa.com/slake.
