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

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arxiv 2102.09542 v1 pith:QOOIELZ7 submitted 2021-02-18 cs.CV cs.AIcs.CL

Bo Liu , Li-Ming Zhan , Li Xu , Lin Ma , Yan Yang , Xiao-Ming Wu This is my paper
classification cs.CVcs.AIcs.CL
keywords slakedatasetmed-vqamedicalansweringdevelopmentevaluationquestion
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 11 Pith papers

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