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arXiv · 2102.09542

SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering

Bo Liu·Li-Ming Zhan·Li Xu·Lin Ma·Yan Yang·Xiao-Ming Wu
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Abstract

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.

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Bo Liu, Li-Ming Zhan, Li Xu, Lin Ma, Yan Yang, Xiao-Ming Wu. 2021-02-18. SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering. https://arxiv.org/abs/2102.09542

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