ResGNet-C: A graph convolutional neural network for detection of COVID-19

[1]  Bo Hu,et al.  Fingerspelling Identification for Chinese Sign Language via AlexNet-Based Transfer Learning and Adam Optimizer , 2020, Sci. Program..

[2]  Yueyang Li,et al.  Seamless indoor pedestrian tracking by fusing INS and UWB measurements via LS-SVM assisted UFIR filter , 2020, Neurocomputing.

[3]  Liang Chang,et al.  Classification of Alzheimer's Disease via Eight-Layer Convolutional Neural Network with Batch Normalization and Dropout Techniques , 2020, J. Medical Imaging Health Informatics.

[4]  Vinay Namboodiri,et al.  CovidAID: COVID-19 Detection Using Chest X-Ray , 2020, ArXiv.

[5]  Jianwen Wang,et al.  Timely Diagnosis and Treatment Shortens the Time to Resolution of Coronavirus Disease (COVID-19) Pneumonia and Lowers the Highest and Last CT Scores From Sequential Chest CT. , 2020, AJR. American journal of roentgenology.

[6]  Fei Shan,et al.  Clinical and CT features of early stage patients with COVID-19: a retrospective analysis of imported cases in Shanghai, China , 2020, European Respiratory Journal.

[7]  K. Wang,et al.  Imaging manifestations and diagnostic value of chest CT of coronavirus disease 2019 (COVID-19) in the Xiaogan area , 2020, Clinical Radiology.

[8]  K. Cao,et al.  Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT , 2020, Radiology.

[9]  Bin Liu,et al.  The characteristics and clinical value of chest CT images of novel coronavirus pneumonia , 2020, Clinical Radiology.

[10]  Xiaolong Qi,et al.  Real estimates of mortality following COVID-19 infection , 2020, The Lancet Infectious Diseases.

[11]  L. Xia,et al.  Coronavirus Disease 2019 (COVID-19): Role of Chest CT in Diagnosis and Management. , 2020, AJR. American journal of roentgenology.

[12]  J. Hageman.  The Coronavirus Disease 2019 (COVID-19). , 2020, Pediatric annals.

[13]  Yicheng Fang,et al.  Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR , 2020, Radiology.

[14]  Bo Xu,et al.  A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19) , 2020, European Radiology.

[15]  Z. Fayad,et al.  CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV) , 2020, Radiology.

[16]  Zhaosong Zhu,et al.  Recognition of Chinese finger sign language via gray-level co-occurrence matrix and K-nearest neighbor algorithm , 2019, 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE).

[17]  Pheng-Ann Heng,et al.  CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[18]  Zhiguo Jiang,et al.  Graph Convolutional Networks for Cervical Cell Classification , 2019 .

[19]  Shui-Hua Wang,et al.  Multi-Scale Feature Combination of Brain Functional Network for eMCI Classification , 2019, IEEE Access.

[20]  Chaosheng Tang,et al.  Hearing loss identification via wavelet entropy and combination of Tabu search and particle swarm optimization , 2018, 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP).

[21]  Yuan Luo,et al.  Graph Convolutional Networks for Text Classification , 2018, AAAI.

[22]  Zhuqing Jiao,et al.  Research on node properties of resting-state brain functional networks by using node activity and ALFF , 2018, Multimedia Tools and Applications.

[23]  Yuriy S. Shmaliy,et al.  Adaptive robust INS/UWB-integrated human tracking using UFIR filter bank , 2018, Measurement.

[24]  Min Cai,et al.  Hub recognition for brain functional networks by using multiple-feature combination , 2018, Comput. Electr. Eng..

[25]  Razvan Pascanu,et al.  Learning Deep Generative Models of Graphs , 2018, ICLR 2018.

[26]  Erik Cambria,et al.  Recent Trends in Deep Learning Based Natural Language Processing , 2017, IEEE Comput. Intell. Mag..

[27]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[28]  M. Bronstein,et al.  Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks , 2017, NIPS.

[29]  Yudong Zhang,et al.  A Pathological Brain Detection System based on Extreme Learning Machine Optimized by Bat Algorithm. , 2017, CNS & neurological disorders drug targets.

[30]  Franco Scarselli,et al.  A Comparative Study of Inductive and Transductive Learning with Feedforward Neural Networks , 2016, AI*IA.

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

[32]  Hui Li,et al.  Digital mammographic tumor classification using transfer learning from deep convolutional neural networks , 2016, Journal of medical imaging.

[33]  Huaizu Jiang,et al.  Face Detection with the Faster R-CNN , 2016, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

[34]  Ming Yang,et al.  A pathological brain detection system based on kernel based ELM , 2016, Multimedia Tools and Applications.

[35]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[38]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[40]  Zhuqing Jiao,et al.  Effective connectivity analysis of fMRI data based on network motifs , 2014, The Journal of Supercomputing.

[41]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[42]  De-Shuang Huang,et al.  A General CPL-AdS Methodology for Fixing Dynamic Parameters in Dual Environments , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[43]  Hervé Glotin,et al.  Cooperative Sparse Representation in Two Opposite Directions for Semi-Supervised Image Annotation , 2012, IEEE Transactions on Image Processing.

[44]  Xiaofeng Wang,et al.  An efficient local Chan-Vese model for image segmentation , 2010, Pattern Recognit..

[45]  Xiaofeng Wang,et al.  A Novel Density-Based Clustering Framework by Using Level Set Method , 2009, IEEE Transactions on Knowledge and Data Engineering.

[46]  De-Shuang Huang,et al.  A Constructive Hybrid Structure Optimization Methodology for Radial Basis Probabilistic Neural Networks , 2008, IEEE Transactions on Neural Networks.

[47]  Ramesh Nallapati,et al.  A Comparative Study of Methods for Transductive Transfer Learning , 2007, Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007).

[48]  De-Shuang Huang,et al.  Zeroing polynomials using modified constrained neural network approach , 2005, IEEE Transactions on Neural Networks.

[49]  De-Shuang Huang,et al.  A Neural Root Finder of Polynomials Based on Root Moments , 2004, Neural Computation.

[50]  De-Shuang Huang,et al.  A constructive approach for finding arbitrary roots of polynomials by neural networks , 2004, IEEE Transactions on Neural Networks.

[51]  D.-S. Huang,et al.  Radial Basis Probabilistic Neural Networks: Model and Application , 1999, Int. J. Pattern Recognit. Artif. Intell..

[52]  Zhuqing Jiao,et al.  Extracting Sub-Networks from Brain Functional Network Using Graph Regularized Nonnegative Matrix Factorization , 2020 .

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

[54]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[55]  Ah Chung Tsoi,et al.  The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.