research-article

An Overview of Disease Prediction based on Graph Convolutional Neural Network

Authors:

Tang LinAuthors Info & Claims

ICIIP '21: Proceedings of the 6th International Conference on Intelligent Information Processing

Pages 27 - 32

https://doi.org/10.1145/3480571.3480575

Published: 29 October 2021 Publication History

Abstract

Geometric deep learning provides a principled and universal way for the integration of imaging and non-imaging modes in the medical field. Graph Convolutional Networks (GCNs) have been widely explored in a variety of problems, such as disease prediction, segmentation, and matrix completion. Using large, multi-modal data sets, graphs can capture the interaction of individual elements represented as nodes in the graphs. In particular, in medical applications, nodes can represent individuals (patients or healthy controls) in a potentially large population and are accompanied by a set of features, while the edges of the graph contain the associations between subjects in an intuitive way. This representation allows the inclusion of rich imaging and non-imaging information as well as individual subject characteristics in the task of disease classification. This article gives an overview of graph convolutional neural networks, focusing on the application of graph convolutional neural networks in disease prediction, and discusses the challenges and prospects of graph convolutional neural networks in disease prediction.

References

[1]

Huang Y, Chung A C S. Edge-Variational Graph Convolutional Networks for Uncertainty-Aware Disease Prediction[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2020: 562-572.

[2]

Parisot S, Ktena S I, Ferrante E, Spectral graph convolutions for population-based disease prediction[C]//International conference on medical image computing and computer-assisted intervention. Springer, Cham, 2017: 177-185.

[3]

Parisot S, Ktena S I, Ferrante E, Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer's disease[J]. Medical image analysis, 2018, 48: 117-130.

[4]

Kazi A, Shekarforoush S, Krishna S A, InceptionGCN: receptive field aware graph convolutional network for disease prediction[C]//International Conference on Information Processing in Medical Imaging. Springer, Cham, 2019: 73-85.

[5]

Jain S M. Detection of Autism using Magnetic Resonance Imaging data and Graph Convolutional Neural Networks[J]. 2018.

[6]

Song T A, Chowdhury S R, Yang F, Graph convolutional neural networks for Alzheimer's disease classification[C]//2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE, 2019: 414-417.

[7]

Arya D, Olij R, Gupta D K, Fusing Structural and Functional MRIs using Graph Convolutional Networks for Autism Classification[C]//Medical Imaging with Deep Learning. PMLR, 2020: 44-61.

[8]

Zhao X, Zhou F, Ou-Yang L, Graph convolutional network analysis for mild cognitive impairment prediction[C]//2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE, 2019: 1598-1601.

[9]

Liu J, Zeng D, Lu M, Mild Cognitive Impairment Identification Based on Multi-View Graph Convolutional Networks[C]//2019 Seventh International Conference on Advanced Cloud and Big Data (CBD). IEEE, 2019: 309-314.

[10]

Liu J, Zeng D, Guo R, Mmhge: detecting mild cognitive impairment based on multi-atlas multi-view hybrid graph convolutional networks and ensemble learning[J]. Cluster Computing, 2021, 24(1): 103-113.

Digital Library

[11]

Liu J, Tan G, Lan W, Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks[J]. BMC bioinformatics, 2020, 21(6): 1-12.

[12]

Bruna J, Zaremba W, Szlam A, LeCun Y. Spectural networks and locally connected networks on graphs//Proceedings of the International Conference on Learning Representations(ICLR2014). Banff, Canada, 2014: URL http://arxiv.org/abs/1312.6203

[13]

Estrach J B, Zaremba W, Szlam A, Spectral networks and deep locally connected networks on graphs[C]//2nd International Conference on Learning Representations, ICLR. 2014, 2014.

[14]

Hammond D K, Vandergheynst P, Gribonval R. Wavelets on graphs via spectral graph theory[J]. Applied and Computational Harmonic Analysis, 2011, 30(2): 129-150.

[15]

Monti F, Boscaini D, Masci J, Geometric deep learning on graph and manifolds using mixture model CNNs//Proceedings of the Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017:5425-5434

[16]

Gilmer J, Schoenholz S S, Riley P F, Neural message passing for quantum chemistry//Proceedings of the 34th International Conference on Machine Learning. Syddney, Australia, 2017:1263_1272

Cited By

Liu XXiao YHuang BZhang YZhang Z(2024)Fuzzy Overlapping Community Guided Subgraph Neural Network for Graph Classification2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650442(1-7)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650442
Lu HUddin S(2023)Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and TrendsHealthcare10.3390/healthcare1107103111:7(1031)Online publication date: 4-Apr-2023
https://doi.org/10.3390/healthcare11071031
Zhou FHu SDu XWan XLu ZWu J(2023)Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing HomesElectronics10.3390/electronics1204100912:4(1009)Online publication date: 17-Feb-2023
https://doi.org/10.3390/electronics12041009

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An Overview of Disease Prediction based on Graph Convolutional Neural Network

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ICIIP '21: Proceedings of the 6th International Conference on Intelligent Information Processing

July 2021

347 pages

ISBN:9781450390637

DOI:10.1145/3480571

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 29 October 2021

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ICIIP 2021

ICIIP 2021: 2021 6th International Conference on Intelligent Information Processing

July 29 - 31, 2021

Bucharest, Romania

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Cited By

Liu XXiao YHuang BZhang YZhang Z(2024)Fuzzy Overlapping Community Guided Subgraph Neural Network for Graph Classification2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650442(1-7)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650442
Lu HUddin S(2023)Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and TrendsHealthcare10.3390/healthcare1107103111:7(1031)Online publication date: 4-Apr-2023
https://doi.org/10.3390/healthcare11071031
Zhou FHu SDu XWan XLu ZWu J(2023)Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing HomesElectronics10.3390/electronics1204100912:4(1009)Online publication date: 17-Feb-2023
https://doi.org/10.3390/electronics12041009

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