Fusion of ULS Group Constrained High- and Low-Order Sparse Functional Connectivity Networks for MCI Classification

Li, Yang; Liu, Jingyu; Peng, Ziwen; Sheng, Can; Kim, Minjeong; Yap, Pew Thian; Wee, Chong Yaw; Shen, Dinggang

doi:10.1007/s12021-019-09418-x

Cited by 20 publications

(10 citation statements)

References 89 publications

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“…However, in many practical applications, including brain imaging, the underlying graph adjacency matrix undergoes a significant variation, as opposed to the graph stationarity assumption [23]. Also, contemporary research in the field of time evolving GSP has discovered the correlation between the alterations in graph connectivity variation and the AD, thus giving rise to a new disease indicator [25][26][27]. To utilize the information in the brain connectivity variation for AD detection, in our previous work [28], we proposed a novel SSM based method to characterize the alterations in the graph connectivity matrices and used it to design a dynamic connectivity model for early diagnosis of AD.…”

Section: Dynamic Connectivity Based Ad Detection Modelmentioning

confidence: 99%

“…However, unlike the theoretical assumption of the static graph connectivity, in many practical applications, including brain imaging, the graph adjacency matrix often varies significantly over time [23,24]. Recently, the researchers working in the paradigm of time varying graph connectivity have discovered the alterations in the dynamics of brain graph connectivity [25][26][27] in case of AD, which makes it a plausible biomarker for early diagnosis of AD. So, to make use of the information present in the graph connectivity variation of a time varying graph for AD detection, in our previous work [28], we designed a dynamic connectivity based AD detection model wherein a novel state-space modelling (SSM) based method was proposed to characterize the time varying nature of the graph.…”

Section: Introductionmentioning

confidence: 99%

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Early Detection of Alzheimer’s Disease Using Graph Signal Processing and Deep Learning

Padole¹,

Joshi²,

Gandhi³

2022

Intelligent Automation &Amp; Soft Computing

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Many methods have been proposed in the literature for diagnosis of Alzheimer's disease (AD) in the early stages, among which the graph-based methods have been more popular, because of their capability to utilize the relational information among different brain regions. Here, we design a novel graph signal processing based integrated AD detection model using multimodal deep learning that simultaneously utilizes both the static and the dynamic brain connectivity based features extracted from resting-state fMRI (rs-fMRI) data to detect AD in the early stages. First, our earlier proposed state-space model (SSM) based graph connectivity dynamics characterization method is used to design a modified dynamic connectivity based AD detection model. After verifying its utility, this dynamic connectivity based model is integrated with our earlier designed static connectivity based AD detection model using the intermediate level integration approach of the multimodal deep learning, to construct our proposed integrated AD detection model. To verify the effectiveness of the designed AD detection models, the models are applied on the rs-fMRI data, extracted from ADNI dataset. Superior performance of our proposed AD diagnosis method corroborates its utility in AD detection application.

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Section: Dynamic Connectivity Based Ad Detection Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Early Detection of Alzheimer’s Disease Using Graph Signal Processing and Deep Learning

Padole¹,

Joshi²,

Gandhi³

2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…Watanabe et al [ 103 ] developed a CAD system using U-Net to detect hematoma and reduce the reading time consumed by the physicians. Sharrock et al [ 104 ] constructed a three-dimensional model based on VNet to segment the regions with both IVH and SDH in CT images. Mansour et al [ 105 ] developed an automated model for ICH classification with the aid of the Inception V4 network for feature extraction and Multilayer Perceptron for five-class labelling.…”

Section: Generics Of Computer Aided Diagnosismentioning

confidence: 99%

“…Irene et al [ 101 ] combined the dynamic graph convolutional neural network model (DGCNN) and SVM with the RBF kernel to compute the hematoma volume, and achieved a mean absolute error of 99.95%. Sharrock et al [ 104 ] used a modified VNet framework to compute the hematoma volume, and achieved a volume correlation of 0.979. Table 6 summarises the different CAD models for ICH volume quantification, namely using voxel resolution of segmented hematoma and the CNN.…”

Section: Generics Of Computer Aided Diagnosismentioning

confidence: 99%

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Vidhya

Gudigar

Raghavendra

et al. 2021

IJERPH

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Traumatic brain injury (TBI) occurs due to the disruption in the normal functioning of the brain by sudden external forces. The primary and secondary injuries due to TBI include intracranial hematoma (ICH), raised intracranial pressure (ICP), and midline shift (MLS), which can result in significant lifetime disabilities and death. Hence, early diagnosis of TBI is crucial to improve patient outcome. Computed tomography (CT) is the preferred modality of choice to assess the severity of TBI. However, manual visualization and inspection of hematoma and its complications from CT scans is a highly operator-dependent and time-consuming task, which can lead to an inappropriate or delayed prognosis. The development of computer aided diagnosis (CAD) systems could be helpful for accurate, early management of TBI. In this paper, a systematic review of prevailing CAD systems for the detection of hematoma, raised ICP, and MLS in non-contrast axial CT brain images is presented. We also suggest future research to enhance the performance of CAD for early and accurate TBI diagnosis.

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“…Liu et al (2018) proposed a deep multi-instance convolutional neural network (CNN) to automatically learn both local and global representations for MR images and achieve superior performance over state-of-the-art approaches. In particular, in MCI classification problems, Yang et al (2014Yang et al ( , 2020a have proposed effective sparse functional connectivity networks and sparse multivariate autoregressive modeling methods for MCI classification. In our previous study (Yao et al, 2019), we trained a CNN to classify different cognitive performances in patients with subcortical ischemic vascular disease (SIVD) on the basis of T2-weighted fluid-attenuated inversion recovery (FLAIR) data.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Model for Classification of Different Subtypes of Subcortical Vascular Cognitive Impairment With FLAIR

et al. 2020

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Deep learning methods have shown their great capability of extracting high-level features from image and have been used for effective medical imaging classification recently. However, training samples of medical images are restricted by the amount of patients as well as medical ethics issues, making it hard to train the neural networks. In this paper, we propose a novel end-to-end three-dimensional (3D) attention-based residual neural network (ResNet) architecture to classify different subtypes of subcortical vascular cognitive impairment (SVCI) with single-shot T2-weighted fluid-attenuated inversion recovery (FLAIR) sequence. Our aim is to develop a convolutional neural network to provide a convenient and effective way to assist doctors in the diagnosis and early treatment of the different subtypes of SVCI. The experiment data in this paper are collected from 242 patients from the Neurology Department of Renji Hospital, including 78 amnestic mild cognitive impairment (a-MCI), 70 nonamnestic MCI (na-MCI), and 94 no cognitive impairment (NCI). The accuracy of our proposed model has reached 98.6% on a training set and 97.3% on a validation set. The test accuracy on an untrained testing set reaches 93.8% with robustness. Our proposed method can provide a convenient and effective way to assist doctors in the diagnosis and early treatment.

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Fusion of ULS Group Constrained High- and Low-Order Sparse Functional Connectivity Networks for MCI Classification

Cited by 20 publications

References 89 publications

Early Detection of Alzheimer’s Disease Using Graph Signal Processing and Deep Learning

Early Detection of Alzheimer’s Disease Using Graph Signal Processing and Deep Learning

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

A Deep Learning-Based Model for Classification of Different Subtypes of Subcortical Vascular Cognitive Impairment With FLAIR

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