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Found 8 papers, 0 papers with code
Dynamic MRI reconstruction using low-rank plus sparse decomposition with smoothness regularization
The low-rank plus sparse (L+S) decomposition model has enabled better reconstruction of dynamic magnetic resonance imaging (dMRI) with separation into background (L) and dynamic (S) component.
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification
Recent applications of pattern recognition techniques on brain connectome classification using functional connectivity (FC) neglect the non-Euclidean topology and causal dynamics of brain connectivity across time.
Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation
Despite its remarkable success for Euclidean-valued data generation, use of standard generative adversarial networks (GANs) to generate manifold-valued FC data neglects its inherent SPD structure and hence the inter-relatedness of edges in real FC.
Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification
Our new framework demonstrates feasibility of learning graph embeddings on brain networks to provide discriminative information for diagnosis of brain disorders.
Kalman Filter and Wavelet Cross-correlation for VHF Broadband Interferometer Lightning Mapping
We perform a comparative study of using different processing techniques and procedures to enhance the localization and mapping of lightning VHF radiation.
Classification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network
We propose a deep convolutional neural network (CNN) framework for classification of electroencephalogram (EEG)-derived brain connectome in schizophrenia (SZ).
Short-segment heart sound classification using an ensemble of deep convolutional neural networks
This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats.
A Markov-Switching Model Approach to Heart Sound Segmentation and Classification
Methods: We propose an approach based on Markov switching autoregressive model (MSAR) to segmenting the HS into four fundamental components each with distinct second-order structure.
