  The increased amount of multi-modal medical data has opened the opportunities
to simultaneously process various modalities such as imaging and non-imaging
data to gain a comprehensive insight into the disease prediction domain. Recent
studies using Graph Convolutional Networks (GCNs) provide novel semi-supervised
approaches for integrating heterogeneous modalities while investigating the
patients' associations for disease prediction. However, when the meta-data used
for graph construction is not available at inference time (e.g., coming from a
distinct population), the conventional methods exhibit poor performance. To
address this issue, we propose a novel semi-supervised approach named GKD based
on knowledge distillation. We train a teacher component that employs the
label-propagation algorithm besides a deep neural network to benefit from the
graph and non-graph modalities only in the training phase. The teacher
component embeds all the available information into the soft pseudo-labels. The
soft pseudo-labels are then used to train a deep student network for disease
prediction of unseen test data for which the graph modality is unavailable. We
perform our experiments on two public datasets for diagnosing Autism spectrum
disorder, and Alzheimer's disease, along with a thorough analysis on synthetic
multi-modal datasets. According to these experiments, GKD outperforms the
previous graph-based deep learning methods in terms of accuracy, AUC, and Macro
F1.
