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Computer Science > Computer Vision and Pattern Recognition

arXiv:2102.09542 (cs)
[Submitted on 18 Feb 2021]

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

Authors: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 this http URL.
Comments: ISBI 2021
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2102.09542 [cs.CV]
  (or arXiv:2102.09542v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2102.09542

Submission history

From: Bo Li [view email]
[v1] Thu, 18 Feb 2021 18:44:50 UTC (5,123 KB)
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