TY - GEN
T1 - Slake: A semantically-labeled knowledge-enhanced dataset for medical visual question answering
AU - Liu, Bo
AU - Zhan, Li Ming
AU - Xu, Li
AU - Ma, Lin
AU - Yang, Yan
AU - Wu, Xiao Ming
N1 - Funding Information:
We would like to thank the anonymous reviewers for their helpful comments. Thanks to Lau et al [1] for their pioneering work in Med-VQA, NIH Clinical Center for sharing their open access dataset [8], and all the doctors and medical students who helped with this research. This research was supported by the grant of P0030935 (ZVPY) funded by PolyU (UGC).
Publisher Copyright:
© 2021 IEEE.
PY - 2021/4/13
Y1 - 2021/4/13
N2 - 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.
AB - 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.
KW - Dataset
KW - Medical visual question answering
KW - Multi-modality fusion.
UR - http://www.scopus.com/inward/record.url?scp=85107173474&partnerID=8YFLogxK
U2 - 10.1109/ISBI48211.2021.9434010
DO - 10.1109/ISBI48211.2021.9434010
M3 - Conference article published in proceeding or book
AN - SCOPUS:85107173474
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1650
EP - 1654
BT - 2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PB - IEEE Computer Society
T2 - 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
Y2 - 13 April 2021 through 16 April 2021
ER -