Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction

Jiang, Hao; Cao, Peng; Xu, Ming; Yang, Jinzhu; Zaı̈ane, Osmar R.

doi:10.1016/j.compbiomed.2020.104096

Cited by 140 publications

(62 citation statements)

References 31 publications

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“…To preserve the the topology information in the population network and their associated individual brain function network, Jiang et al [ 66 ] proposed a hierarchical GCN framework to map the brain network to a low-dimensional vector while preserving the topology information. Their method leveraged a correlation mechanism in populating the network which could capture more information and result in more accurate brain network representation, and thus better classification of ASD from the ABIDE dataset [ 63 ] in comparison to Eigenpooling GCN [ 80 ] and the other population GCN [ 72 ] methods.…”

Section: Case Studies Of Gnn For Medical Diagnosis and Analysismentioning

confidence: 99%

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Ahmedt-Aristizabal

Armin

Denman

et al. 2021

Sensors

149

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With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered, which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be determined by either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.

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Section: Case Studies Of Gnn For Medical Diagnosis and Analysismentioning

confidence: 99%

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Ahmedt-Aristizabal

Armin

Denman

et al. 2021

Sensors

149

View full text Add to dashboard Cite

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“…It can also generate more stable network metrics compared to the absolute thresholding [44]. It has been shown that the setting of threshold τ has a significant impact on the overall performance of the network classification model [36]. Besides, when τ decreases, networks become sparser and may lead to the zero-degree nodes (isolated nodes totally disconnected from the rest of the graph).…”

Section: A Connectivity Network Constructionmentioning

confidence: 99%

“…A spectral-based GCN has been proposed to perform convolutions in the graph spatial domain as multiplications in the graph spectral domain [31], [32]. Applications of spectral GCNs to brain disorder detection from brain functional networks are introduced only recently and in its very early stage, e.g., for predicting ASD and conversion from MCI to AD [33]- [36]. These studies used a population graph as input to GCN, where nodes represent subjects with associated resting-state FC feature vectors, while phenotype information is encoded as graph edge weights.…”

mentioning

confidence: 99%

Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification

Noman¹,

Chen²,

Kang³

et al. 2021

Preprint

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Brain functional connectivity (FC) reveals biomarkers for identification of various neuropsychiatric disorders. Recent application of deep neural networks (DNNs) to connectomebased classification mostly relies on traditional convolutional neural networks using input connectivity matrices on a regular Euclidean grid. We propose a graph deep learning framework to incorporate the non-Euclidean information about graph structure for classifying functional magnetic resonance imaging (fMRI)derived brain networks in major depressive disorder (MDD). We design a novel graph autoencoder (GAE) architecture based on the graph convolutional networks (GCNs) to embed the topological structure and node content of large-sized fMRI networks into low-dimensional latent representations. In network construction, we employ the Ledoit-Wolf (LDW) shrinkage method to estimate the high-dimensional FC metrics efficiently from fMRI data. We consider both supervised and unsupervised approaches for the graph embedded learning. The learned embeddings are then used as feature inputs for a deep fully-connected neural network (FCNN) to discriminate MDD from healthy controls. Evaluated on a resting-state fMRI MDD dataset with 43 subjects, results show that the proposed GAE-FCNN model significantly outperforms several state-of-the-art DNN methods for brain connectome classification, achieving accuracy of 72.50% using the LDW-FC metrics as node features. The graph embeddings of fMRI FC networks learned by the GAE also reveal apparent group differences between MDD and HC. Our new framework demonstrates feasibility of learning graph embeddings on brain networks to provide discriminative information for diagnosis of brain disorders.

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“…Hi-GCN [24] uses a hierarchical GCN to learn the graph feature embedding for the classification of ASD and AD.…”

Section: Related Work a Gcns For Disease Predictionmentioning

confidence: 99%

FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction

Peng,

Wang,

Dvornek

et al. 2021

Preprint

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Graph Convolutional Neural Networks (GCNs) are widely used for graph analysis. Specifically, in medical applications, GCNs can be used for disease prediction on a population graph, where graph nodes represent individuals and edges represent individual similarities. However, GCNs rely on a vast amount of data, which is challenging to collect for a single medical institution. In addition, a critical challenge that most medical institutions continue to face is addressing disease prediction in isolation with incomplete data information. To address these issues, Federated Learning (FL) allows isolated local institutions to collaboratively train a global model without data sharing. In this work, we propose a framework, FedNI, to leverage network inpainting and inter-institutional data via FL. Specifically, we first federatively train missing node and edge predictor using a graph generative adversarial network (GAN) to complete the missing information of local networks. Then we train a global GCN node classifier across institutions using a federated graph learning platform. The novel design enables us to build more accurate machine learning models by leveraging federated learning and also graph learning approaches. We demonstrate that our federated model outperforms local and baseline FL methods with significant margins on two public neuroimaging datasets.

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Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction

Cited by 140 publications

References 31 publications

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification

FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction

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