A Question-Centric Model for Visual Question Answering in Medical Imaging
This addresses the need for more interpretable AI in clinical settings, though it is an incremental improvement over existing VQA methods.
The authors tackled the lack of transparency in deep learning models for medical imaging by developing a Visual Question Answering approach that allows querying images with written questions, achieving equal or higher accuracy compared to current methods on various datasets.
Deep learning methods have proven extremely effective at performing a variety of medical image analysis tasks. With their potential use in clinical routine, their lack of transparency has however been one of their few weak points, raising concerns regarding their behavior and failure modes. While most research to infer model behavior has focused on indirect strategies that estimate prediction uncertainties and visualize model support in the input image space, the ability to explicitly query a prediction model regarding its image content offers a more direct way to determine the behavior of trained models. To this end, we present a novel Visual Question Answering approach that allows an image to be queried by means of a written question. Experiments on a variety of medical and natural image datasets show that by fusing image and question features in a novel way, the proposed approach achieves an equal or higher accuracy compared to current methods.