Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-modal Graph Fusion for Inductive Disease Classification in
  Incomplete Datasets   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.

5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
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The categories of this paper are: atom-ph