mlsanityData to 7 Oct 2026

GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysis

arXiv 2203.09034 ↗First posted 17 Mar 2022cs.LG eess.IVcited by 19

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Abstract (arXiv)

In this work, we focus on the challenging task, neuro-disease classification, using functional magnetic resonance imaging (fMRI). In population graph-based disease analysis, graph convolutional neural networks (GCNs) have achieved remarkable success. However, these achievements are inseparable from abundant labeled data and sensitive to spurious signals. To improve fMRI representation learning and classification under a label-efficient setting, we propose a novel and theory-driven self-supervised learning (SSL) framework on GCNs, namely Graph CCA for Temporal self-supervised learning on fMRI analysis GATE. Concretely, it is demanding to design a suitable and effective SSL strategy to extract formation and robust features for fMRI. To this end, we investigate several new graph augmentation strategies from fMRI dynamic functional connectives (FC) for SSL training. Further, we leverage canonical-correlation analysis (CCA) on different temporal embeddings and present the theoretical implications. Consequently, this yields a novel two-step GCN learning procedure comprised of (i) SSL on an unlabeled fMRI population graph and (ii) fine-tuning on a small labeled fMRI dataset for a classification task. Our method is tested on two independent fMRI datasets, demonstrating superior performance on autism and dementia diagnosis.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 32 of its 64 references that are in the map. First posted on arXiv 17 Mar 2022.

Builds on

32 of 64 references.

References · 32

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20 more
  1. 2017Deep Learning for Medical Image Analysiscited by 399
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  3. 2021Large-Scale Representation Learning on Graphs via Bootstrappingcited by 184
  4. 2020Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Datacited by 179
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  6. 2021From Canonical Correlation Analysis to Self-supervised Graph Neural Networkscited by 138
  7. 2018Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Diseasecited by 98
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 5; 2025: 7; 2026: 4.

19 papers.

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  1. 2023LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matchingcited by 60
  2. 2024MM-GTUNets: Unified Multi-Modal Graph Deep Learning for Brain Disorders Predictioncited by 9
  3. 2022Benchmarking Graph Neural Networks for FMRI analysiscited by 4
  4. 2025CIMAGE: Exploiting the Conditional Independence in Masked Graph Auto-encoderscited by 4
  5. 2025Physics-Guided Multi-View Graph Neural Network for Schizophrenia Classification via Structural-Functional Couplingcited by 3
  6. 2025Information Bottleneck-Guided Heterogeneous Graph Learning for Interpretable Neurodevelopmental Disorder Diagnosiscited by 2
  7. 2023Parkinson's Disease Classification Using Contrastive Graph Cross-View Learning with Multimodal Fusion of SPECT Images and Clinical Featurescited by 1
  8. 2024Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic reviewcited by 1
  9. 2024MCDGLN: Masked Connection-based Dynamic Graph Learning Network for Autism Spectrum Disordercited by 1
  10. 2025Identity Preserving Latent Diffusion for Brain Aging Modelingcited by 1
  11. 2025VarCoNet: A variability-aware self-supervised framework for functional connectome extraction from resting-state fMRIcited by 1
  12. 2026Fine-Grained Traceability for Transparent ML Pipelinescited by 1
7 more
  1. 2024Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive Learningcited by 0
  2. 2024HyperBrain: Anomaly Detection for Temporal Hypergraph Brain Networkscited by 0
  3. 2025scBIT: Integrating Single-cell Transcriptomic Data into fMRI-based Prediction for Alzheimer's Disease Diagnosiscited by 0
  4. 2025Data-Efficient Psychiatric Disorder Detection via Self-supervised Learning on Frequency-enhanced Brain Networkscited by 0
  5. 2026Network-Aware Bilinear Tokenization for Brain Functional Connectivity Representation Learningcited by 0
  6. 2026HyperAMS-Net: Adaptive Multi-Scale Spatial Hypergraph Network for Brain Disorder Classificationcited by 0
  7. 2026Decoupling Disease, Covariates, and Individual Variability: A Unified Disentanglement Framework for Medical Image Classificationcited by 0