InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction

Kazi, Anees; Shekarforoush, Shayan; Krishna, Sruthi; Burwinkel, Hendrik; Vivar, Gerome; Kortüm, Karsten; Ahmadi, Seyed-Ahmad; Albarqouni, Shadi; Navab, Nassir

doi:10.1007/978-3-030-20351-1_6

Cited by 127 publications

(119 citation statements)

References 14 publications

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“…As one of the most famous graph networks, GCN mainly applies the convolution of Fourier transform and Taylor's expansion formula to improve filtering performance (34). With its excellent performance, GCN has been widely used in disease classification (34)(35)(36)(37)(38).…”

Section: Gcnmentioning

confidence: 99%

“…Zhang et al ( 36 ) combined an adaptive pooling scheme and a multimodal mechanism to classify Parkinson's disease (PD) status. Kazi et al ( 37 ) designed different kernel sizes in spectral convolution to learn cluster-specific features for predicting mild cognitive impairment and AD. All these studies validate the effectiveness of GCN and show that the convolution operation is the key to prediction performance.…”

Section: Related Workmentioning

confidence: 99%

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Diagnosis of COVID-19 Pneumonia Based on Graph Convolutional Network

et al. 2021

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A three-dimensional (3D) deep learning method is proposed, which enables the rapid diagnosis of coronavirus disease 2019 (COVID-19) and thus significantly reduces the burden on radiologists and physicians. Inspired by the fact that the current chest computed tomography (CT) datasets are diversified in equipment types, we propose a COVID-19 graph in a graph convolutional network (GCN) to incorporate multiple datasets that differentiate the COVID-19 infected cases from normal controls. Specifically, we first apply a 3D convolutional neural network (3D-CNN) to extract image features from the initial 3D-CT images. In this part, a transfer learning method is proposed to improve the performance, which uses the task of predicting equipment type to initialize the parameters of the 3D-CNN structure. Second, we design a COVID-19 graph in GCN based on the extracted features. The graph divides all samples into several clusters, and samples with the same equipment type compose a cluster. Then we establish edge connections between samples in the same cluster. To compute accurate edge weights, we propose to combine the correlation distance of the extracted features and the score differences of subjects from the 3D-CNN structure. Lastly, by inputting the COVID-19 graph into GCN, we obtain the final diagnosis results. In experiments, the dataset contains 399 COVID-19 infected cases, and 400 normal controls from six equipment types. Experimental results show that the accuracy, sensitivity, and specificity of our method reach 98.5%, 99.9%, and 97%, respectively.

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Section: Gcnmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Diagnosis of COVID-19 Pneumonia Based on Graph Convolutional Network

et al. 2021

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“…Further, we aim to provide users the flexibility to interpret different levels of biomarkers through graph node pooling and several innovative loss terms to regulate the pooling operation. In addition, different from much of the GNN literature [47,34] where populational graphs based on fMRI are modeled by treating each subject as a node on the graph, we model each subject's brain as one graph and each brain ROI as a node to learn ROI-based graph embeddings. Specifically, our framework jointly learns ROI clustering and the whole-brain fMRI prediction.…”

Section: Introductionmentioning

confidence: 99%

“…Due to their 25 high performance and interpretability, GNNs have been a widely applied graph 26 analysis method. [26,25,49,28,50]. Most existing GNNs are built on graphs that 27 do not have correspondence between the nodes of different instances, such as 28 social networks and protein networks, limiting interpretability.…”

mentioning

confidence: 99%

“…HCP Dataset For this dataset, we restrict our analyses to those individuals 284 who participated in all nine fMRI conditions (seven tasks, two rests) with full 285 length of scan, whose mean frame-to-frame displacement is less than 0.1 mm 286 and whose maximum frame-to-frame displacement is less than 0.15 mm (n=506; 287 237 males; ages [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. This conservative threshold for exclusion due to motion 288 is used to mitigate the substantial effects of motion on functional connectivity; 289 only left-right (LR) phase encoding run data are considered.…”

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confidence: 99%

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BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis

Zhou

Gao

et al. 2020

Preprint

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The brain is an exceptionally complex system and understanding it's functional 2 organization is the goal of modern neuroscience. Using fMRI, large strides in 3 understanding this organization have been made by modeling the brain as a 4 graph-a mathematical construct describing the connections or interactions (i.e. 5 edges) between different discrete objects (i.e. nodes). To create these graphs, 6 nodes are defined as brain regions of interest (ROIs) and edges are defined as the 7 functional connectivity between those ROIs, computed as the pairwise correla-8 tions of functional magnetic resonance imaging (fMRI) time series, as illustrated 9 in Fig. 1. Traditional graph-based analyses for fMRI have focused on using graph 10 theoretical metrics to summarize the functional connectivity for each node into 11 a single number [45,23]. However, these methods do not consider higher-order 12 interactions between ROIs, as these interactions cannot be preserved in a sin-13 gle number. Additionally, due to the high dimensionality of fMRI data, usually 14 ROIs are clustered into highly connected communities to reduce dimensionality. 15Then, features are extracted from these smaller communities for further analysis 16 [31,12]. For these two-stage methods, if the results from the first stage are not 17 reliable, significant errors can be induced in the second stage. 18The past few years have seen the growing prevalence of the use of graph 19 neural networks (GNN) for end-to-end graph learning applications. GNNs are 20 the state-of-the-art deep learning methods for most graph-structured data anal-21 ysis problems. They combine node features, edge features, and graph structure 22 by using a neural network to embed node information and pass information 23 through edges in the graph. As such, they can be viewed as a generalization of 24 the traditional convolutional neural networks (CNN) for images. Due to their 25 high performance and interpretability, GNNs have been a widely applied graph 26 analysis method. [26,25,49,28,50]. Most existing GNNs are built on graphs that 27 do not have correspondence between the nodes of different instances, such as 28 social networks and protein networks, limiting interpretability. These methods 29-including the current GNN methods for fMRI analysis -use the same ker-30 nel over different nodes, which implicitly assumes brain graphs are translation 31 invariant. However, nodes in the same brain graph have distinct locations and 32 unique identities. Thus, applying the same kernel over all nodes is problematic. 33In addition, few GNN studies have explored both individual-level and group-level 34 explanations, which are critical in neuroimaging research. 35In this work, we propose a graph neural network-based framework for map-36 ping regional and cross-regional functional activation patterns for classification 37 tasks, such as classifying neurodisorder patients versus healthy control subjects 38 and performing cognitive task decoding. Our framework jointly learns ROI clus-39 tering and the downstr...

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MTGWNN: A Multi‐Template Graph Wavelet Neural Network Identification Model for Autism Spectrum Disorder

Shan,

Ren,

Jiao

et al. 2024

Int J Imaging Syst Tech

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Functional magnetic resonance imaging (fMRI) has been widely applied in studying various brain disorders. However, current studies typically model regions of interest (ROIs) in brains with a single template. This approach generally examines only the connectivity between ROIs to identify autism spectrum disorder (ASD), ignoring the structural features of the brain. This study proposes a multi‐template graph wavelet neural network (GWNN) identification model for ASD called MTGWNN. First, the brain is segmented with multiple templates and the BOLD time series are extracted from fMRI data to construct brain networks. Next, a graph attention network (GAT) is applied to automatically learn interactions between nodes, capturing local information in the node features. These features are then further processed by a convolutional neural network (CNN) to learn global connectivity representations and achieve feature dimensionality reduction. Finally, the features and phenotypic data from each subject are integrated by GWNN to identify ASD at the optimal scale. Experimental results indicate that MTGWNN outperforms the comparative models. Testing on the public dataset ABIDE‐I achieved an accuracy (ACC) of 87.25% and an area under the curve (AUC) of 92.49%. MTGWNN effectively integrates brain network features from multiple templates, providing a more comprehensive characterization of brain abnormalities in patients with ASD. It incorporates population information from phenotypic data, which helps to compensate for the limited sample size of individual patients and improves the robustness and generalization of ASD identification.

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InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction

Cited by 127 publications

References 14 publications

Diagnosis of COVID-19 Pneumonia Based on Graph Convolutional Network

Diagnosis of COVID-19 Pneumonia Based on Graph Convolutional Network

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis

MTGWNN: A Multi‐Template Graph Wavelet Neural Network Identification Model for Autism Spectrum Disorder

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