  Clinical diagnostic decision making and population-based studies often rely
on multi-modal data which is noisy and incomplete. Recently, several works
proposed geometric deep learning approaches to solve disease classification, by
modeling patients as nodes in a graph, along with graph signal processing of
multi-modal features. Many of these approaches are limited by assuming
modality- and feature-completeness, and by transductive inference, which
requires re-training of the entire model for each new test sample. In this
work, we propose a novel inductive graph-based approach that can generalize to
out-of-sample patients, despite missing features from entire modalities per
patient. We propose multi-modal graph fusion which is trained end-to-end
towards node-level classification. We demonstrate the fundamental working
principle of this method on a simplified MNIST toy dataset. In experiments on
medical data, our method outperforms single static graph approach in
multi-modal disease classification.
