  Interpretability in Graph Convolutional Networks (GCNs) has been explored to
some extent in computer vision in general, yet, in the medical domain, it
requires further examination. Moreover, most of the interpretability approaches
for GCNs, especially in the medical domain, focus on interpreting the model in
a post hoc fashion. In this paper, we propose an interpretable graph
learning-based model which 1) interprets the clinical relevance of the input
features towards the task, 2) uses the explanation to improve the model
performance and, 3) learns a population level latent graph that may be used to
interpret the cohort's behavior. In a clinical scenario, such a model can
assist the clinical experts in better decision-making for diagnosis and
treatment planning. The main novelty lies in the interpretable attention module
(IAM), which directly operates on multi-modal features. Our IAM learns the
attention for each feature based on the unique interpretability-specific
losses. We show the application on two publicly available datasets, Tadpole and
UKBB, for three tasks of disease, age, and gender prediction. Our proposed
model shows superior performance with respect to compared methods with an
increase in an average accuracy of 3.2% for Tadpole, 1.6% for UKBB Gender, and
2% for the UKBB Age prediction task. Further, we show exhaustive validation and
clinical interpretation of our results.
