# Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction

arXiv: https://arxiv.org/abs/2009.02759 · HTML: https://mlsanity.com/paper/2009.02759/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2020.

## Built on by (most cited first, 25 of 25)

- [Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future](https://mlsanity.com/paper/2105.13137/) (2021)
- [A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective](https://mlsanity.com/paper/2209.13232/) (2022)
- [A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods](https://mlsanity.com/paper/2310.06873/) (2023)
- [Multi-modal Graph Learning for Disease Prediction](https://mlsanity.com/paper/2203.05880/) (2022)
- [Review of multimodal machine learning approaches in healthcare](https://mlsanity.com/paper/2402.02460/) (2024)
- [FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction](https://mlsanity.com/paper/2112.10166/) (2021)
- [Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review](https://mlsanity.com/paper/2303.06471/) (2023)
- [Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification](https://mlsanity.com/paper/2107.12838/) (2021)
- [RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data](https://mlsanity.com/paper/2103.00221/) (2021)
- [Continual Learning on Graphs: A Survey](https://mlsanity.com/paper/2402.06330/) (2024)
- [Unsupervised pre-training of graph transformers on patient population graphs](https://mlsanity.com/paper/2207.10603/) (2022)
- [Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications](https://mlsanity.com/paper/2202.08916/) (2022)
- [AMA-GCN: Adaptive Multi-layer Aggregation Graph Convolutional Network for Disease Prediction](https://mlsanity.com/paper/2106.08732/) (2021)
- [GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference](https://mlsanity.com/paper/2104.03597/) (2021)
- [Multimodal brain age estimation using interpretable adaptive population-graph learning](https://mlsanity.com/paper/2307.04639/) (2023)
- [Enhanced Graph Convolutional Network with Chebyshev Spectral Graph and Graph Attention for Autism Spectrum Disorder Classification](https://mlsanity.com/paper/2511.22178/) (2025)
- [Multimodal Brain Disease Classification with Functional Interaction Learning from Single fMRI Volume](https://mlsanity.com/paper/2208.03028/) (2022)
- [Multi-modal Graph Learning for Disease Prediction](https://mlsanity.com/paper/2107.00206/) (2021)
- [Towards Temporal Edge Regression: A Case Study on Agriculture Trade Between Nations](https://mlsanity.com/paper/2308.07883/) (2023)
- [Classification of developmental and brain disorders via graph convolutional aggregation](https://mlsanity.com/paper/2311.07370/) (2023)
- [Spatial-Temporal DAG Convolutional Networks for End-to-End Joint Effective Connectivity Learning and Resting-State fMRI Classification](https://mlsanity.com/paper/2312.10317/) (2023)
- [MLC-GCN: Multi-Level Generated Connectome Based GCN for AD Analysis](https://mlsanity.com/paper/2408.03358/) (2024)
- [A Brain-to-Population Graph Learning Framework for Diagnosing Brain Disorders](https://mlsanity.com/paper/2506.16096/) (2025)
- [Long-Term Alzheimers Disease Prediction: A Novel Image Generation Method Using Temporal Parameter Estimation with Normal Inverse Gamma Distribution on Uneven Time Series](https://mlsanity.com/paper/2511.21057/) (2025)
- [Multimodal Connectome Fusion via Cross-Attention for Autism Spectrum Disorder Classification Using Graph Learning](https://mlsanity.com/paper/2603.15168/) (2026)

## Abstract (arXiv)

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.