Consistency-preserving Visual Question Answering in Medical Imaging
This work addresses the critical issue of trustworthiness in medical VQA systems, which is essential for patient engagement and clinical second opinions, though it is incremental as it builds on existing VQA methods by adding consistency constraints.
The paper tackles the problem of answer inconsistency in medical Visual Question Answering (VQA) by proposing a novel loss function and training procedure that incorporates known relations between questions, specifically for Diabetic Macular Edema staging from fundus imaging, resulting in improved model consistency and overall accuracy compared to state-of-the-art baselines.
Visual Question Answering (VQA) models take an image and a natural-language question as input and infer the answer to the question. Recently, VQA systems in medical imaging have gained popularity thanks to potential advantages such as patient engagement and second opinions for clinicians. While most research efforts have been focused on improving architectures and overcoming data-related limitations, answer consistency has been overlooked even though it plays a critical role in establishing trustworthy models. In this work, we propose a novel loss function and corresponding training procedure that allows the inclusion of relations between questions into the training process. Specifically, we consider the case where implications between perception and reasoning questions are known a-priori. To show the benefits of our approach, we evaluate it on the clinically relevant task of Diabetic Macular Edema (DME) staging from fundus imaging. Our experiments show that our method outperforms state-of-the-art baselines, not only by improving model consistency, but also in terms of overall model accuracy. Our code and data are available at https://github.com/sergiotasconmorales/consistency_vqa.