OVQA: A Clinically Generated Visual Question Answering Dataset

Medical visual question answering (Med-VQA) is a challenging problem that aims to take a medical image and a clinical question about the image as input and output a correct answer in natural language. Current medical systems often require large-scale and high-quality labeled data for training and evaluation. To address the challenge, we present a new dataset, denoted by OVQA, which is generated from electronic medical records. We develop a semi-automatic data generation tool for constructing the dataset. First, medical entities are automatically extracted from medical records and filled into predefined templates for generating question and answer pairs. These pairs are then combined with medical images extracted from corresponding medical records, to generate candidates for visual question answering (VQA). The candidates are finally verified with high-quality labels annotated by experienced physicians. To evaluate the quality of OVQA, we conduct comprehensive experiments on state-of-the-art methods for the Med-VQA task to our dataset. The results show that our OVQA can be used as a benchmarking dataset for evaluating existing Med-VQA systems. The dataset can be downloaded from http://47.94.174.82/.