Search Results for author:
Found 11 papers, 2 papers with code
Self-Supervised Mental Disorder Classifiers via Time Reversal
In this paper, we demonstrate that a model trained on the time direction of functional neuro-imaging data could help in any downstream task, for example, classifying diseases from healthy controls in fMRI data.
Fusion Subspace Clustering for Incomplete Data
This paper introduces {\em fusion subspace clustering}, a novel method to learn low-dimensional structures that approximate large scale yet highly incomplete data.
Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series
To bridge this gap, we developed dynamic effective connectivity estimation via neural network training (DECENNT), a novel model to learn an interpretable directed and dynamic graph induced by the downstream classification/prediction task.
A deep learning model for data-driven discovery of functional connectivity
Functional connectivity (FC) studies have demonstrated the overarching value of studying the brain and its disorders through the undirected weighted graph of fMRI correlation matrix.
Multi network InfoMax: A pre-training method involving graph convolutional networks
Since almost every DL model is an ensemble of multiple networks, we take our high-level embeddings from two different networks of a model --a convolutional and a graph network--.
Brain dynamics via Cumulative Auto-Regressive Self-Attention
The supervised training of the model as a classifier between patients and controls results in a model that generates directed connectivity graphs and highlights the components of the time-series that are predictive for each subject.
Attend to connect: end-to-end brain functional connectivity estimation
Functional connectivity (FC) studies have demonstrated the benefits of investigating the brain and its disorders through the undirected weighted graph of fMRI correlation matrix.
Detecting Spurious Correlations with Sanity Tests for Artificial Intelligence Guided Radiology Systems
Artificial intelligence (AI) has been successful at solving numerous problems in machine perception.
Whole MILC: generalizing learned dynamics across tasks, datasets, and populations
In this paper we present a novel self supervised training schema which reinforces whole sequence mutual information local to context (whole MILC).
Transfer Learning of fMRI Dynamics
In this paper, we demonstrate a self-supervised pre-training method that enables us to pre-train directly on fMRI dynamics of healthy control subjects and transfer the learning to much smaller datasets of schizophrenia.
Fusion Subspace Clustering: Full and Incomplete Data
Subspace clustering achieves this through a union of linear subspaces.
