RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data

[1]  Ben Glocker,et al.  Spectral Graph Convolutions for Population-based Disease Prediction , 2017, MICCAI.

[2]  Samy Bengio,et al.  Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks , 2019, KDD.

[3]  Yunqian Ma,et al.  Imbalanced Learning: Foundations, Algorithms, and Applications , 2013 .

[4]  Jaime S. Cardoso,et al.  A Class Imbalance Ordinal Method for Alzheimer’s Disease Classification , 2018, 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI).

[5]  Rich Caruana,et al.  Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping , 2000, NIPS.

[6]  Pierre Vandergheynst,et al.  Geometric Deep Learning: Going beyond Euclidean data , 2016, IEEE Signal Process. Mag..

[7]  Arvid Lundervold,et al.  An overview of deep learning in medical imaging focusing on MRI , 2018, Zeitschrift fur medizinische Physik.

[8]  David S. Rosenblum,et al.  Directed Graph Convolutional Network , 2020, ArXiv.

[9]  Tianlong Chen,et al.  L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[11]  Kwanghoon Sohn,et al.  SumGraph: Video Summarization via Recursive Graph Modeling , 2020, ECCV.

[12]  Rushil Anirudh,et al.  Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification , 2017, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[13]  Zhengang Jiang,et al.  Imbalanced biomedical data classification using self-adaptive multilayer ELM combined with dynamic GAN , 2018, BioMedical Engineering OnLine.

[14]  Gautam Srivastava,et al.  Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques , 2019, IEEE Access.

[15]  Wenwu Zhu,et al.  Deep Learning on Graphs: A Survey , 2018, IEEE Transactions on Knowledge and Data Engineering.

[16]  Pietro Liò,et al.  Graph Attention Networks , 2017, ICLR.

[17]  Charles E. Leisersen,et al.  EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs , 2019, AAAI.

[18]  Seyed-Ahmad Ahmadi,et al.  Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC) , 2020, Artif. Intell. Medicine.

[19]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[20]  Pascal Frossard,et al.  The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains , 2012, IEEE Signal Processing Magazine.

[21]  Muhammad Yasir,et al.  Deep Convolution Neural Network for Big Data Medical Image Classification , 2020, IEEE Access.

[22]  Takanori Maehara,et al.  Revisiting Graph Neural Networks: All We Have is Low-Pass Filters , 2019, ArXiv.

[23]  Nassir Navab,et al.  Uncertainty-based Graph Convolutional Networks for Organ Segmentation Refinement , 2019, MIDL.

[24]  Yang Song,et al.  Class-Balanced Loss Based on Effective Number of Samples , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Progress in Computing, Analytics and Networking , 2020, Advances in Intelligent Systems and Computing.

[26]  Albert C. S. Chung,et al.  Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction , 2020, MICCAI.

[27]  Aaron C. Courville,et al.  Generative adversarial networks , 2020 .

[28]  Michael W. Kattan,et al.  A comprehensive data level analysis for cancer diagnosis on imbalanced data , 2019, J. Biomed. Informatics.

[29]  Joan Bruna,et al.  Spectral Networks and Locally Connected Networks on Graphs , 2013, ICLR.

[30]  Shadi Albarqouni,et al.  Graph Convolution Based Attention Model for Personalized Disease Prediction , 2019, MICCAI.

[31]  Wei Wang,et al.  A Graph Convolutional Matrix Completion Method for miRNA-Disease Association Prediction , 2020, ICIC.

[32]  Shadi Albarqouni,et al.  InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction , 2019, IPMI.

[33]  Evgeny Burnaev,et al.  Influence of resampling on accuracy of imbalanced classification , 2015, International Conference on Machine Vision.

[34]  Zhi-Hua Zhou,et al.  Exploratory Undersampling for Class-Imbalance Learning , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[35]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[36]  Seetha Hari,et al.  Learning From Imbalanced Data , 2019, Advances in Computer and Electrical Engineering.

[37]  Qiang Cheng,et al.  Exploiting Edge Features in Graph Neural Networks. , 2018 .

[38]  Jure Leskovec,et al.  Inductive Representation Learning on Large Graphs , 2017, NIPS.

[39]  Qi Dong,et al.  Imbalanced Deep Learning by Minority Class Incremental Rectification. , 2019, IEEE transactions on pattern analysis and machine intelligence.

[40]  P. Expert,et al.  Geometric graphs from data to aid classification tasks with Graph Convolutional Networks , 2020, Patterns.

[41]  Luís Torgo,et al.  A Survey of Predictive Modeling on Imbalanced Domains , 2016, ACM Comput. Surv..

[42]  Jiashi Feng,et al.  Zoom in to where it matters: a hierarchical graph based model for mammogram analysis , 2019, ArXiv.

[43]  Jenna L. Pappalardo,et al.  Disease state prediction from single-cell data using graph attention networks , 2020, CHIL.

[44]  Richard K. G. Do,et al.  Convolutional neural networks: an overview and application in radiology , 2018, Insights into Imaging.

[45]  Deepak Gupta,et al.  A Survey on Medical Diagnosis of Diabetes Using Machine Learning Techniques , 2018, Advances in Intelligent Systems and Computing.

[46]  Haizhou Li,et al.  A Cost-Sensitive Deep Belief Network for Imbalanced Classification , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[47]  David S. Wishart,et al.  Applications of Machine Learning in Cancer Prediction and Prognosis , 2006, Cancer informatics.

[48]  Philip S. Yu,et al.  A Comprehensive Survey on Graph Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[49]  Qimai Li,et al.  Label Efficient Semi-Supervised Learning via Graph Filtering , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[50]  Richard S. Johannes,et al.  Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus , 1988 .

[51]  Francisco Herrera,et al.  Learning from Imbalanced Data Sets , 2018, Springer International Publishing.

[52]  Hamid R. Rabiee,et al.  MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional Networks , 2018, 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[53]  Moncef Gabbouj,et al.  Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).

[54]  Paul Honeine,et al.  Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks , 2020, ArXiv.

[55]  Manal Alghamdi,et al.  Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project , 2017, PloS one.

[56]  Ching-Hsue Cheng,et al.  A novel weighted distance threshold method for handling medical missing values , 2020, Comput. Biol. Medicine.

[57]  Tjeng Wawan Cenggoro,et al.  Deep Learning for Imbalance Data Classification using Class Expert Generative Adversarial Network , 2018, ArXiv.

[58]  M. Bronstein,et al.  Latent-Graph Learning for Disease Prediction , 2020, MICCAI.

[59]  Kwok Leung Tsui,et al.  A Framework of Rebalancing Imbalanced Healthcare Data for Rare Events' Classification: A Case of Look-Alike Sound-Alike Mix-Up Incident Detection , 2018, Journal of healthcare engineering.

[60]  Mahmut T. Kandemir,et al.  GCN meets GPU: Decoupling "When to Sample" from "How to Sample" , 2020, NeurIPS.

[61]  Davide Eynard,et al.  SIGN: Scalable Inception Graph Neural Networks , 2020, ArXiv.

[62]  Pan Wang,et al.  PacketCGAN: Exploratory Study of Class Imbalance for Encrypted Traffic Classification Using CGAN , 2019, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).

[63]  Tadahiro Kuroda,et al.  Balanced Mini-Batch Training for Imbalanced Image Data Classification with Neural Network , 2018, 2018 First International Conference on Artificial Intelligence for Industries (AI4I).

[64]  Jin Liu,et al.  Inferring LncRNA-disease associations based on graph autoencoder matrix completion , 2020, Comput. Biol. Chem..

[65]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[66]  Rushi Longadge,et al.  Class Imbalance Problem in Data Mining Review , 2013, ArXiv.

[67]  Nicholas Ho,et al.  Parkinson’s progression prediction using machine learning and serum cytokines , 2019, npj Parkinson's Disease.

[68]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[69]  Andrea Montanari,et al.  A Low-Cost Method for Multiple Disease Prediction , 2015, AMIA.

[70]  Juntang Zhuang,et al.  BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis , 2020, bioRxiv.

[71]  Luca Antiga,et al.  Automatic differentiation in PyTorch , 2017 .

[72]  Buyue Qian,et al.  Graph Neural Network-Based Diagnosis Prediction , 2020, Big Data.

[73]  Isabelle Guyon,et al.  Design of experiments for the NIPS 2003 variable selection benchmark , 2003 .

[74]  Andrew K. C. Wong,et al.  A Weight-Selection Strategy on Training Deep Neural Networks for Imbalanced Classification , 2017, ICIAR.

[75]  Jianxun Liu,et al.  Multi-Class Imbalanced Graph Convolutional Network Learning , 2020, IJCAI.

[76]  A. Singleton,et al.  The Parkinson Progression Marker Initiative (PPMI) , 2011, Progress in Neurobiology.

[77]  Xavier Bresson,et al.  Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.

[78]  Mohammad Ali Moni,et al.  Comparing different supervised machine learning algorithms for disease prediction , 2019, BMC Medical Informatics and Decision Making.

[79]  Sajid Ahmed,et al.  CUSBoost: Cluster-Based Under-Sampling with Boosting for Imbalanced Classification , 2017, 2017 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS).

[80]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[81]  Magudeeswaran Veluchamy,et al.  A fast and effective method for enhancement of contrast resolution properties in medical images , 2020, Multimedia Tools and Applications.

[82]  Alejandro F. Frangi,et al.  Graph convolution network with similarity awareness and adaptive calibration for disease-induced deterioration prediction , 2020, Medical Image Anal..

[83]  Si Zhang,et al.  Graph convolutional networks: a comprehensive review , 2019, Computational Social Networks.

[84]  Eyad Elyan,et al.  Improved Overlap-based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson's Disease , 2020, Int. J. Neural Syst..

[85]  Andrew K. C. Wong,et al.  Classification of Imbalanced Data: a Review , 2009, Int. J. Pattern Recognit. Artif. Intell..

[86]  Heikki Huttunen,et al.  Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects , 2015, NeuroImage.

[87]  Nassir Navab,et al.  Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images , 2018, BrainLes@MICCAI.

[88]  Hsien-I Lin,et al.  Boosting Minority Class Prediction on Imbalanced Point Cloud Data , 2020, Applied Sciences.

[89]  Taghi M. Khoshgoftaar,et al.  Survey on deep learning with class imbalance , 2019, J. Big Data.