On the public citation graph
Sparse structure deep network embedding for transforming brain functional network in early mild cognitive impairment classification
0 signed · 56 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1001/archpsyc.63.8.916openalex-snapshot
Neuropsychological Prediction of Conversion to Alzheimer Disease in Patients With Mild Cognitive Impairment
inferredcites →https://doi.org/10.1002/hbm.22058openalex-snapshot
Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques
inferredcites →https://doi.org/10.1007/978-3-030-30493-5_54openalex-snapshot
Graph Classification with 2D Convolutional Neural Networks
inferredcites →https://doi.org/10.1007/978-3-642-33418-4_27openalex-snapshot
Constrained Sparse Functional Connectivity Networks for MCI Classification
inferredcites →https://doi.org/10.1007/s10916-017-0845-xopenalex-snapshot
Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling
inferredcites →https://doi.org/10.1007/s11042-017-4554-8openalex-snapshot
Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed
inferredcites →https://doi.org/10.1007/s11042-017-5243-3openalex-snapshot
Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation
inferredcites →https://doi.org/10.1007/s11042-018-5765-3openalex-snapshot
Twelve-layer deep convolutional neural network with stochastic pooling for tea category classification on GPU platform
inferredcites →https://doi.org/10.1007/s11554-017-0717-0openalex-snapshot
Polarimetric synthetic aperture radar image segmentation by convolutional neural network using graphical processing units
inferredcites →https://doi.org/10.1016/j.bspc.2015.05.014openalex-snapshot
Detection of Alzheimer's disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC
inferredcites →https://doi.org/10.1016/j.jneumeth.2012.10.012openalex-snapshot
Decoding brain states using backward edge elimination and graph kernels in fMRI connectivity networks
inferredcites →https://doi.org/10.1016/j.jocs.2018.05.005openalex-snapshot
Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic pooling
inferredcites →https://doi.org/10.1016/j.jocs.2018.07.003openalex-snapshot
Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU
inferredcites →https://doi.org/10.1016/j.media.2018.11.006openalex-snapshot
Multimodal hyper-connectivity of functional networks using functionally-weighted LASSO for MCI classification
inferredcites →https://doi.org/10.1016/j.neuroimage.2014.06.077openalex-snapshot
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis
inferredcites →https://doi.org/10.1016/j.neuroimage.2016.09.046openalex-snapshot
BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment
inferredcites →https://doi.org/10.1038/nrn3136openalex-snapshot
Normalization as a canonical neural computation
inferredcites →https://doi.org/10.1038/s41598-017-06509-0openalex-snapshot
Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis
inferredcites →https://doi.org/10.1093/bioinformatics/btz967openalex-snapshot
Morbigenous brain region and gene detection with a genetically evolved random neural network cluster approach in late mild cognitive impairment
inferredcites →https://doi.org/10.1093/cercor/bhr099openalex-snapshot
Decoding Subject-Driven Cognitive States with Whole-Brain Connectivity Patterns
inferredcites →https://doi.org/10.1109/access.2019.2920978openalex-snapshot
Multi-Scale Feature Combination of Brain Functional Network for eMCI Classification
inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot
Fully convolutional networks for semantic segmentation
inferredcites →https://doi.org/10.1109/jbhi.2017.2655720openalex-snapshot
Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease
inferredcites →https://doi.org/10.1109/jbhi.2020.2973324openalex-snapshot
Multimodal Data Analysis of Alzheimer's Disease Based on Clustering Evolutionary Random Forest
inferredcites →https://doi.org/10.1109/surv.2013.013013.00155openalex-snapshot
Virtual Network Embedding: A Survey
inferredcites →https://doi.org/10.1109/tbme.2018.2824725openalex-snapshot
Brain-Wide Genome-Wide Association Study for Alzheimer's Disease via Joint Projection Learning and Sparse Regression Model
inferredcites →https://doi.org/10.1109/tkde.2018.2849727openalex-snapshot
A Survey on Network Embedding
inferredcites →https://doi.org/10.1109/tmi.2019.2913158openalex-snapshot
Latent Representation Learning for Alzheimer’s Disease Diagnosis With Incomplete Multi-Modality Neuroimaging and Genetic Data
inferredcites →https://doi.org/10.1109/tpami.2017.2699184openalex-snapshot
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
inferredcites →https://doi.org/10.1109/wacv.2018.00162openalex-snapshot
Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery
inferredcites →https://doi.org/10.1145/2783258.2783296openalex-snapshot
Heterogeneous Network Embedding via Deep Architectures
inferredcites →https://doi.org/10.1145/2939672.2939753openalex-snapshot
Structural Deep Network Embedding
inferredcites →https://doi.org/10.1166/jmihi.2019.2692openalex-snapshot
High Performance Multiple Sclerosis Classification by Data Augmentation and AlexNet Transfer Learning Model
inferredcites →https://doi.org/10.2174/156720501303160217113858openalex-snapshot
Functional Activity and Connectivity Differences of Five Resting-State Networks in Patients with Alzheimer's Disease or Mild Cognitive Impairment
inferredcites →https://doi.org/10.23919/mva.2017.7986898openalex-snapshot
Dilated convolutions for image classification and object localization
inferredcites →https://doi.org/10.2741/4562openalex-snapshot
Directed connectivity of brain default networks in resting state using GCA and motif
inferredcites →https://doi.org/10.32604/cmes.2020.08999openalex-snapshot
Extracting Sub-Networks from Brain Functional Network Using Graph Regularized Nonnegative Matrix Factorization
inferredcites →https://doi.org/10.3390/e22020239openalex-snapshot
Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer’s Disease: A Review
inferredcites →https://doi.org/10.48550/arxiv.1402.3722openalex-snapshot
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
inferredcites →https://doi.org/10.48550/arxiv.1605.05273openalex-snapshot
Learning Convolutional Neural Networks for Graphs
inferredcites →https://doi.org/10.48550/arxiv.1606.09375openalex-snapshot
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
inferredcites →https://doi.org/10.5281/zenodo.1318390openalex-snapshot
Advances In Deep Learning On Graphs (Gsp'18 Workshop)
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4614678openalex-extracted
Predicting Cognitive Function Status in Esrd Patients at the Functional Subnetwork Scale
inferred← citeshttps://doi.org/10.1016/j.eswa.2026.131952openalex-snapshot
A multi-modal hybrid phenotype-aware graph framework for neuropsychiatric disorder diagnosis
inferred← citeshttps://doi.org/10.3389/fcell.2021.813996openalex-snapshot
RETRACTED: BCNet: A Novel Network for Blood Cell Classification
inferred← citeshttps://doi.org/10.3389/fnagi.2022.834331openalex-snapshot
GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease
inferred← citeshttps://doi.org/10.3389/fpsyt.2022.861258openalex-snapshot
Research on Pathogenic Hippocampal Voxel Detection in Alzheimer's Disease Using Clustering Genetic Random Forest
inferred← citeshttps://doi.org/10.3389/fpsyt.2022.862958openalex-snapshot
A Triple-Network Dynamic Connection Study in Alzheimer's Disease
inferred← citeshttps://doi.org/10.3934/mbe.2024171openalex-snapshot
Predicting the cognitive function status in end-stage renal disease patients at a functional subnetwork scale
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.