no code implementations • 29 Mar 2024 • Ziyu Zhou, Anton Orlichenko, Gang Qu, Zening Fu, Vince D Calhoun, Zhengming Ding, Yu-Ping Wang
Both functional and structural magnetic resonance imaging (fMRI and sMRI) are widely used for the diagnosis of mental disorder.
no code implementations • 12 Feb 2024 • Bradley T. Baker, Mustafa S. Salman, Zening Fu, Armin Iraji, Elizabeth Osuch, Jeremy Bockholt, Vince D. Calhoun
In the clinical treatment of mood disorders, the complex behavioral symptoms presented by patients and variability of patient response to particular medication classes can create difficulties in providing fast and reliable treatment when standard diagnostic and prescription methods are used.
no code implementations • 29 Nov 2022 • Zafar Iqbal, Usman Mahmood, Zening Fu, Sergey Plis
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.
no code implementations • 12 Nov 2022 • Yuda Bi, Anees Abrol, Zening Fu, Vince Calhoun
Vision Transformer (ViT) is a pioneering deep learning framework that can address real-world computer vision issues, such as image classification and object recognition.
no code implementations • 15 Sep 2022 • Yuda Bi, Anees Abrol, Zening Fu, Jiayu Chen, Jingyu Liu, Vince Calhoun
Prior work has demonstrated that deep learning models that take advantage of the data's 3D structure can outperform standard machine learning on several learning tasks.
no code implementations • 4 Feb 2022 • Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis
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.
1 code implementation • 7 Dec 2021 • Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis
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.
no code implementations • 1 Nov 2021 • Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis
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--.
no code implementations • 1 Nov 2021 • Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis
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.
no code implementations • 3 May 2021 • Eloy Geenjaar, Noah Lewis, Zening Fu, Rohan Venkatdas, Sergey Plis, Vince Calhoun
Neuroimaging studies often involve the collection of multiple data modalities.
no code implementations • ICLR Workshop GTRL 2021 • Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis
Functional connectivity (FC) studies have demonstrated the benefits of investigating the brain and its disorders through the undirected weighted graph of fMRI correlation matrix.
1 code implementation • 29 Jul 2020 • Usman Mahmood, Md Mahfuzur Rahman, Alex Fedorov, Noah Lewis, Zening Fu, Vince D. Calhoun, Sergey M. Plis
In this paper we present a novel self supervised training schema which reinforces whole sequence mutual information local to context (whole MILC).
no code implementations • 16 Nov 2019 • Usman Mahmood, Md Mahfuzur Rahman, Alex Fedorov, Zening Fu, Sergey Plis
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.
no code implementations • 24 Apr 2019 • Alex Fedorov, R. Devon Hjelm, Anees Abrol, Zening Fu, Yuhui Du, Sergey Plis, Vince D. Calhoun
Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging.