18 citations · 35 across the 10 of their papers we have counts for
10 papers
Self-Supervised Mental Disorder Classifiers via Time Reversal
Zafar Iqbal, Usman Mahmood, Zening Fu +1
Data scarcity is a notable problem, especially in the medical domain, due to patient data laws. Therefore, efficient Pre-Training techniques could help in combating this problem. I…
Fusion Subspace Clustering for Incomplete Data
Usman Mahmood, Daniel Pimentel-Alarcón
This paper introduces {\em fusion subspace clustering}, a novel method to learn low-dimensional structures that approximate large scale yet highly incomplete data. The main idea is…
Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series
Usman Mahmood, Zening Fu, Vince Calhoun +1
Recently, methods that represent data as a graph, such as graph neural networks (GNNs) have been successfully used to learn data representations and structures to solve classificat…
A deep learning model for data-driven discovery of functional connectivity
Usman Mahmood, Zening Fu, Vince Calhoun +1
Functional connectivity (FC) studies have demonstrated the overarching value of studying the brain and its disorders through the undirected weighted graph of fMRI correlation matri…
Multi network InfoMax: A pre-training method involving graph convolutional networks
Usman Mahmood, Zening Fu, Vince Calhoun +1
Discovering distinct features and their relations from data can help us uncover valuable knowledge crucial for various tasks, e.g., classification. In neuroimaging, these features…
Brain dynamics via Cumulative Auto-Regressive Self-Attention
Usman Mahmood, Zening Fu, Vince Calhoun +1
Multivariate dynamical processes can often be intuitively described by a weighted connectivity graph between components representing each individual time-series. Even a simple repr…
Whole MILC: generalizing learned dynamics across tasks, datasets, and populations
Usman Mahmood, Md Mahfuzur Rahman, Alex Fedorov +4
Behavioral changes are the earliest signs of a mental disorder, but arguably, the dynamics of brain function gets affected even earlier. Subsequently, spatio-temporal structure of…
Learnt dynamics generalizes across tasks, datasets, and populations
U. Mahmood, M. M. Rahman, A. Fedorov +3
Differentiating multivariate dynamic signals is a difficult learning problem as the feature space may be large yet often only a few training examples are available. Traditional app…
Transfer Learning of fMRI Dynamics
Usman Mahmood, Md Mahfuzur Rahman, Alex Fedorov +2
As a mental disorder progresses, it may affect brain structure, but brain function expressed in brain dynamics is affected much earlier. Capturing the moment when brain dynamics ex…
Fusion Subspace Clustering: Full and Incomplete Data
Daniel L. Pimentel-Alarcón, Usman Mahmood
Modern inference and learning often hinge on identifying low-dimensional structures that approximate large scale data. Subspace clustering achieves this through a union of linear s…