Medical VQA

Inspired by the rise of VQA research in general domain, the task of Medical VQA has received great attention from computer vision, natural language processing and biomedical research communities in recent years. Given a medical image and clinically related question about the visual elements in the medical image, a Medical VQA system is required to deeply comprehend both the medical image and the asked question to predict the correct answer. In this chapter, we first introduce mainstream datasets used for Medical VQA tasks, such as VQA-RAD, VQA-Med, PathVQA and SLAKE datasets. Then, we elaborate the prevalent methods for Medical VQA tasks in detail. These methods can be classified into three categories based on their main characteristics: classical VQA methods, meta-learning methods and BERT-based methods for Medical VQA.