# Adaptive Image-Feature Learning for Disease Classification Using Inductive Graph Networks

## TL;DR

This paper introduces an end-to-end inductive graph neural network architecture for disease classification that jointly trains image and graph filters, leading to improved accuracy and stability over existing methods.

## Contribution

The proposed architecture enables joint training of CNN and graph filters in an end-to-end manner, enhancing disease classification performance.

## Key findings

- Significantly improved classification scores on a modified MNIST dataset.
- Achieved comparable results with higher stability on chest X-ray images.
- Demonstrated the impact of graph structure on feature learning.

## Abstract

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.