# Multi-modal Graph Fusion for Inductive Disease Classification in Incomplete Datasets

arXiv: https://arxiv.org/abs/1905.03053 · HTML: https://mlsanity.com/paper/1905.03053/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2019.

## Built on by (most cited first, 1 of 1)

- [IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction](https://mlsanity.com/paper/2103.15587/) (2021)

## Abstract (arXiv)

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.