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arXiv · 1905.03053

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

Gerome Vivar·Hendrik Burwinkel·Anees Kazi·Andreas Zwergal·Nassir Navab·Seyed-Ahmad Ahmadi
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Abstract

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.

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Gerome Vivar, Hendrik Burwinkel, Anees Kazi, Andreas Zwergal, Nassir Navab, Seyed-Ahmad Ahmadi. 2019-05-08. Multi-modal Graph Fusion for Inductive Disease Classification in Incomplete Datasets. https://arxiv.org/abs/1905.03053

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