Multi-modal Graph Fusion for Inductive Disease Classification in Incomplete Datasets
This work addresses clinical diagnostic challenges by enabling more flexible and efficient disease classification in noisy, incomplete datasets, though it appears incremental as it builds on existing graph-based methods.
The authors tackled disease classification with incomplete multi-modal data by proposing an inductive graph-based approach that generalizes to new patients despite missing entire modalities, outperforming single static graph methods.
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.