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Found 5 papers, 1 papers with code
Leveraging Pre-Images to Discover Nonlinear Relationships in Multivariate Environments
We show that our method outperforms state-of-the-art causal discovery methods when the observations are restricted by time and are nonlinearly related.
Large-scale Augmented Granger Causality (lsAGC) for Connectivity Analysis in Complex Systems: From Computer Simulations to Functional MRI (fMRI)
Our results suggest that lsAGC, by extracting sparse connectivity matrices, may be useful for network analysis in complex systems, and may be applicable to clinical fMRI analysis in future research, such as targeting disease-related classification or regression tasks on clinical data.
Large-Scale Extended Granger Causality for Classification of Marijuana Users From Functional MRI
We use brain connections estimated by lsXGC as features for classification.
Large-scale kernelized GRANGER causality to infer topology of directed graphs with applications to brain networks
Graph topology inference of network processes with co-evolving and interacting time-series is crucial for network studies.
MRI Tumor Segmentation with Densely Connected 3D CNN
The major difficulty of our segmentation model comes with the fact that the location, structure, and shape of gliomas vary significantly among different patients.
