Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Abstract

With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.

References in corpus (90)

• A Survey on Deep Learning in Medical Image Analysis
• A Comprehensive Survey on Graph Neural Networks
• Semi-Supervised Classification with Graph Convolutional Networks
• LSTM: A Search Space Odyssey
• The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
• Inductive Representation Learning on Large Graphs
• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• Geometric deep learning: going beyond Euclidean data
• Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
• ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• Spectral Networks and Locally Connected Networks on Graphs
• Convolutional Sequence to Sequence Learning
• Striving for Simplicity: The All Convolutional Net
• A Critical Review of Recurrent Neural Networks for Sequence Learning
• Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
• Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
• Simplifying Graph Convolutional Networks
• REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
• The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies
• Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease
• Learning Convolutional Neural Networks for Graphs
• A large annotated medical image dataset for the development and evaluation of segmentation algorithms
• Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
• GNNExplainer: Generating Explanations for Graph Neural Networks
• Complex network approaches to nonlinear time series analysis
• Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
• Semantic Graph Convolutional Networks for 3D Human Pose Regression
• Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
• GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
• FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
• Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
• Contrastive Multi-View Representation Learning on Graphs
• Self-Attention Graph Pooling
• A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis
• Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
• Deep Vessel Segmentation By Learning Graphical Connectivity
• MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs
• SIGN: Scalable Inception Graph Neural Networks
• Stochastic Training of Graph Convolutional Networks with Variance Reduction
• GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals
• GraphGAN: Graph Representation Learning with Generative Adversarial Nets
• Efficient Graph Generation with Graph Recurrent Attention Networks
• InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
• A Survey on Graph-Based Deep Learning for Computational Histopathology
• GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
• DeepGCNs: Can GCNs Go as Deep as CNNs?
• TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer's Disease
• On Explainability of Graph Neural Networks via Subgraph Explorations
• Relation Modeling with Graph Convolutional Networks for Facial Action Unit Detection
• Graph WaveNet for Deep Spatial-Temporal Graph Modeling
• TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data
• Spherical CNNs on Unstructured Grids
• When Does Self-Supervision Help Graph Convolutional Networks?
• Human Pose Estimation with Spatial Contextual Information
• Multi-Label Image Recognition with Graph Convolutional Networks
• Explainability Techniques for Graph Convolutional Networks
• Uncertainty Aware Semi-Supervised Learning on Graph Data
• Temporal Graph Convolutional Networks for Automatic Seizure Detection
• PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks
• EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network
• Predicting Alzheimer's Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging
• GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs
• Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning
• Towards Explainable Graph Representations in Digital Pathology
• Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning
• Fast Interactive Object Annotation with Curve-GCN
• Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks
• Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease
• Zoom in to where it matters: a hierarchical graph based model for mammogram analysis
• Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals
• DeepSphere: a graph-based spherical CNN
• Extraction of Airways using Graph Neural Networks
• Uncertainty quantification of molecular property prediction with Bayesian neural networks
• Differentiable Hierarchical Graph Grouping for Multi-Person Pose Estimation
• Temporal Graph Modeling for Skeleton-based Action Recognition
• Graph Neural Network for Interpreting Task-fMRI Biomarkers
• Memory-Based Graph Networks
• Feedback Graph Attention Convolutional Network for Medical Image Enhancement
• MER-GCN: Micro Expression Recognition Based on Relation Modeling with Graph Convolutional Network
• GOSH: Embedding Big Graphs on Small Hardware
• Deep neural networks on graph signals for brain imaging analysis
• Brain2Object: Printing Your Mind from Brain Signals with Spatial Correlation Embedding
• Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network
• Seeing Under the Cover: A Physics Guided Learning Approach for In-Bed Pose Estimation
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation
• Towards a predictive spatio-temporal representation of brain data
• Learnable Pooling in Graph Convolution Networks for Brain Surface Analysis

Cited by in corpus (16)

• A Survey on Graph-Based Deep Learning for Computational Histopathology
• A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
• Anomaly Detection in Dynamic Graphs: A Comprehensive Survey
• Artificial Intelligence-based algorithms in medical image scan seg-mentation and intelligent visual-content generation -- a concise overview
• Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter
• NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
• A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks
• Private Graph Extraction via Feature Explanations
• Leveraging Medical Foundation Model Features in Graph Neural Network-Based Retrieval of Breast Histopathology Images
• Meta Transfer of Self-Supervised Knowledge: Foundation Model in Action for Post-Traumatic Epilepsy Prediction
• Releasing Graph Neural Networks with Differential Privacy Guarantees
• XInsight: Revealing Model Insights for GNNs with Flow-based Explanations
• Node-Level Differentially Private Graph Neural Networks
• Automated Coronary Arteries Labeling Via Geometric Deep Learning
• DuoGNN: Topology-aware Graph Neural Network with Homophily and Heterophily Interaction-Decoupling
• A Structural Feature-Based Approach for Comprehensive Graph Classification