  Recently, Geometric Deep Learning (GDL) has been introduced as a novel and
versatile framework for computer-aided disease classification. GDL uses patient
meta-information such as age and gender to model patient cohort relations in a
graph structure. Concepts from graph signal processing are leveraged to learn
the optimal mapping of multi-modal features, e.g. from images to disease
classes. Related studies so far have considered image features that are
extracted in a pre-processing step. We hypothesize that such an approach
prevents the network from optimizing feature representations towards achieving
the best performance in the graph network. We propose a new network
architecture that exploits an inductive end-to-end learning approach for
disease classification, where filters from both the CNN and the graph are
trained jointly. We validate this architecture against state-of-the-art
inductive graph networks and demonstrate significantly improved classification
scores on a modified MNIST toy dataset, as well as comparable classification
results with higher stability on a chest X-ray image dataset. Additionally, we
explain how the structural information of the graph affects both the image
filters and the feature learning.
