The report of SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering [ISBI 2021]. We build 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
GEMeX: GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis
The report of GEMeX: GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis. We introduce GEMeX, a large-scale Med-VQA dataset for chest X-rays, designed to support diverse question types and provide enhanced explainability for medical VQA systems. To our knowledge, it is the largest chest X-ray VQA dataset and the first Med-VQA dataset to embody the concept of multimodal explainability.
