Free Form Medical Visual Question Answering in Radiology
This research addresses a gap in medical VQA for radiology, offering potential applications in diagnostic settings, though it appears incremental as it builds on existing datasets and methods.
The paper tackled the challenge of medical visual question answering in radiology by developing a model that effectively represents radiology images and learns multimodal representations, achieving a top-1 accuracy of 79.55% with a less complex architecture.
Visual Question Answering (VQA) in the medical domain presents a unique, interdisciplinary challenge, combining fields such as Computer Vision, Natural Language Processing, and Knowledge Representation. Despite its importance, research in medical VQA has been scant, only gaining momentum since 2018. Addressing this gap, our research delves into the effective representation of radiology images and the joint learning of multimodal representations, surpassing existing methods. We innovatively augment the SLAKE dataset, enabling our model to respond to a more diverse array of questions, not limited to the immediate content of radiology or pathology images. Our model achieves a top-1 accuracy of 79.55\% with a less complex architecture, demonstrating comparable performance to current state-of-the-art models. This research not only advances medical VQA but also opens avenues for practical applications in diagnostic settings.