  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.
