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.
@article{arxiv.2102.09542,
title = {SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering},
author = {Bo Liu and Li-Ming Zhan and Li Xu and Lin Ma and Yan Yang and Xiao-Ming Wu},
journal= {arXiv preprint arXiv:2102.09542},
year = {2021}
}