Node Based Row-Filter Convolutional Neural Network for Brain Network Classification

Mao, Bingcheng; Huang, Jiashuang; Zhang, Daoqiang

doi:10.1007/978-3-319-97304-3_82

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…These methods fall into three categories: fMRI-based methods, DTI-based methods, fMRI- and DTI-based methods. More specifically, fMRI-based methods are Pearson coefficient (PC) (Betzel et al, 2016 ), low-rank sparse representation (LSR) (Qiao et al, 2016 ), weighted sparse group representation (WSGR) (Yu et al, 2017 ), Strength and Similarity GSR (SSGSR) (Zhang et al, 2019 ), high-order FC (HOFC) (Chen et al, 2016 ), topographic FC (tHOFC) (Zhang et al, 2016a ), Graph-CNN (GCNN) (Mao et al, 2018 ), and Siamese-GCN (SGCN) (Ktena et al, 2017 ). DTI-based methods are Graph kernel (GK) (Kang et al, 2012 ), Graph-CNN (GCNN) (Mao et al, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

“…More specifically, fMRI-based methods are Pearson coefficient (PC) (Betzel et al, 2016 ), low-rank sparse representation (LSR) (Qiao et al, 2016 ), weighted sparse group representation (WSGR) (Yu et al, 2017 ), Strength and Similarity GSR (SSGSR) (Zhang et al, 2019 ), high-order FC (HOFC) (Chen et al, 2016 ), topographic FC (tHOFC) (Zhang et al, 2016a ), Graph-CNN (GCNN) (Mao et al, 2018 ), and Siamese-GCN (SGCN) (Ktena et al, 2017 ). DTI-based methods are Graph kernel (GK) (Kang et al, 2012 ), Graph-CNN (GCNN) (Mao et al, 2018 ). fMRI- and DTI-based methods are multi-kernel (MK) (Dyrba et al, 2015 ), our methods without space alignment, our method without node importance information, and our proposed methods (JCFBN).…”

Section: Methodsmentioning

confidence: 99%

“…In the GCNN (Mao et al, 2018 ) model, a specific convolutional operator is designed for brain network which applies a row scanning on adjacent matrix to generate the feature map. Classification results are acquired by the softmax function based on these feature maps.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Multimodal Brain Network Jointly Construction and Fusion for Diagnosis of Epilepsy

Zhu

Yang²,

Xu³

et al. 2021

Front. Neurosci.

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Brain network analysis has been proved to be one of the most effective methods in brain disease diagnosis. In order to construct discriminative brain networks and improve the performance of disease diagnosis, many machine learning–based methods have been proposed. Recent studies show that combining functional and structural brain networks is more effective than using only single modality data. However, in the most of existing multi-modal brain network analysis methods, it is a common strategy that constructs functional and structural network separately, which is difficult to embed complementary information of different modalities of brain network. To address this issue, we propose a unified brain network construction algorithm, which jointly learns both functional and structural data and effectively face the connectivity and node features for improving classification. First, we conduct space alignment and brain network construction under a unified framework, and then build the correlation model among all brain regions with functional data by low-rank representation so that the global brain region correlation can be captured. Simultaneously, the local manifold with structural data is embedded into this model to preserve the local structural information. Second, the PageRank algorithm is adaptively used to evaluate the significance of different brain regions, in which the interaction of multiple brain regions is considered. Finally, a multi-kernel strategy is utilized to solve the data heterogeneity problem and merge the connectivity as well as node information for classification. We apply the proposed method to the diagnosis of epilepsy, and the experimental results show that our method can achieve a promising performance.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Multimodal Brain Network Jointly Construction and Fusion for Diagnosis of Epilepsy

Zhu

Yang²,

Xu³

et al. 2021

Front. Neurosci.

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show abstract

“…We compared the proposed GIGCN method with several methods, including, Node Based Row-Filter Convolutional Neural Network (NRF-CNN) [7], Siamese-GCN (sGCN) [6], Disease Prediction Graph Convolutional Networks (DP-GCN) [8]. NRF-CNN [7] and sGCN [6] are two works construct graph at the brain region level, i.e., the internal brain graph method.…”

Section: Performance Of Our Full Methods On Brain Disease Classificationmentioning

confidence: 99%

“…Based on the difference of graph construction strategies, the GCNbased brain disease diagnosis studies can be roughly divided into the following two categories. The first category follows the graph classification paradigm which constructs graph at the brain region level, where nodes represent brain regions and edges incorporate associations between brain regions [6,7]. The algorithms in the second category treat each subject as a single node, so that the associations or similarities between subjects can be represented by graph edges.…”

Section: Introductionmentioning

confidence: 99%

Graph-In-Graph Convolutional Networks For Brain Disease Diagnosis

Zhou

Zhang

2021

2021 IEEE International Conference on Image Processing (ICIP)

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So far, in the study of neurological brain disorder diagnosis, there are mainly two kinds of work that exploits graph via Graph Convolutional Networks (GCN). Though they have achieved remarkable success, neither of them is able to simultaneously account for the brain region level correlation and subject level correlation. To tackle this issue, we design a Graph-In-Graph Convolutional Networks(GIGCN) framework, which turns out to inherit the merits of the two kinds of existing work. Specifically, we propose a graph-in-graph data structure that consists of two types of graph, namely internal brain connective graph and external population graph. In this data structure, an internal graph is assigned to each subject whose nodes represent brain regions and edges incorporate associations between brain regions, while the external graph is a population graph generated from the demographic of all subjects where edges describe the similarity between subjects. After that, our proposed framework employs GCN to explore some deeper relationships for better disease prediction. Results on the ABIDE dataset validate the effectiveness of our proposed method.

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Integrating Functional and Structural Connectivities via Diffusion-Convolution-Bilinear Neural Network

Huang

Zhou

Wang

et al. 2019

Lecture Notes in Computer Science

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Node Based Row-Filter Convolutional Neural Network for Brain Network Classification

Cited by 7 publications

References 8 publications

Multimodal Brain Network Jointly Construction and Fusion for Diagnosis of Epilepsy

Multimodal Brain Network Jointly Construction and Fusion for Diagnosis of Epilepsy

Graph-In-Graph Convolutional Networks For Brain Disease Diagnosis

Integrating Functional and Structural Connectivities via Diffusion-Convolution-Bilinear Neural Network

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