SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering
This addresses the data scarcity problem for researchers and developers in medical AI, though it is incremental as it builds upon existing Med-VQA efforts by expanding modalities and coverage.
The authors tackled the lack of publicly-available, high-quality datasets for medical visual question answering (Med-VQA) by introducing SLAKE, a large bilingual dataset with comprehensive semantic labels annotated by physicians and a new structural medical knowledge base, which facilitates development and evaluation of Med-VQA systems.
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.