Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction
This addresses the problem of multimodal data integration for computer-aided disease diagnosis, offering a generalizable solution with uncertainty awareness, though it appears incremental as it builds on graph convolutional networks.
The paper tackled the challenge of integrating imaging and non-imaging data for disease prediction by proposing a framework with a learnable adaptive population graph and Monte-Carlo edge dropout for uncertainty estimation, achieving significant diagnostic accuracy improvements on four databases for Autism spectrum disorder, Alzheimer's disease, and ocular diseases.
There is a rising need for computational models that can complementarily leverage data of different modalities while investigating associations between subjects for population-based disease analysis. Despite the success of convolutional neural networks in representation learning for imaging data, it is still a very challenging task. In this paper, we propose a generalizable framework that can automatically integrate imaging data with non-imaging data in populations for uncertainty-aware disease prediction. At its core is a learnable adaptive population graph with variational edges, which we mathematically prove that it is optimizable in conjunction with graph convolutional neural networks. To estimate the predictive uncertainty related to the graph topology, we propose the novel concept of Monte-Carlo edge dropout. Experimental results on four databases show that our method can consistently and significantly improve the diagnostic accuracy for Autism spectrum disorder, Alzheimer's disease, and ocular diseases, indicating its generalizability in leveraging multimodal data for computer-aided diagnosis.