Search Results for author:
Found 11 papers, 6 papers with code
DeepGraphDMD: Interpretable Spatio-Temporal Decomposition of Non-linear Functional Brain Network Dynamics
In this work, we apply GraphDMD -- an extension of the DMD for network data -- to extract the dynamic network modes and their temporal characteristics from the fMRI time series in an interpretable manner.
Anomaly Detection via Gumbel Noise Score Matching
We propose Gumbel Noise Score Matching (GNSM), a novel unsupervised method to detect anomalies in categorical data.
Harmonization Benchmarking Tool for Neuroimaging Datasets
A major data pre-processing step for large, multi-site studies is to handle site effects by harmonizing data, generating a dataset that enables more powerful analyses and more robust algorithms.
Optimal Transport Features for Morphometric Population Analysis
We demonstrate the proposed optimal transport feature extraction step in the context of a volumetric morphometric analysis of the OASIS-1 study for Alzheimer's disease.
Multi-view Attention for gestational age at birth prediction
We present our method for gestational age at birth prediction for the SLCN (surface learning for clinical neuroimaging) challenge.
Non-Euclidean Analysis of Joint Variations in Multi-Object Shapes
This paper considers joint analysis of multiple functionally related structures in classification tasks.
Multiscale Score Matching for Out-of-Distribution Detection
We present a new methodology for detecting out-of-distribution (OOD) images by utilizing norms of the score estimates at multiple noise scales.
Learning Common Harmonic Waves on Stiefel Manifold -- A New Mathematical Approach for Brain Network Analyses
To that end, we propose a novel connectome harmonic analysis framework to provide enhanced mathematical insights by detecting frequency-based alterations relevant to brain disorders.
Accurate Automatic Segmentation of Amygdala Subnuclei and Modeling of Uncertainty via Bayesian Fully Convolutional Neural Network
However, most of the previous deep learning work does not investigate the specific difficulties that exist in segmenting extremely small but important brain regions such as the amygdala and its subregions.
Fast Predictive Multimodal Image Registration
We introduce a deep encoder-decoder architecture for image deformation prediction from multimodal images.
Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach
A deep encoder-decoder network is used as the prediction model.
