Search Results for author: Nicha C. Dvornek

Found 17 papers, 3 papers with code

Estimating Reproducible Functional Networks Associated with Task Dynamics using Unsupervised LSTMs

no code implementations6 May 2021 Nicha C. Dvornek, Pamela Ventola, James S. Duncan

We demonstrate that the functional networks learned by the LSTM model are more strongly associated with the task activity and dynamics compared to other approaches.

Time Series

A Metamodel Structure For Regression Analysis: Application To Prediction Of Autism Spectrum Disorder Severity

no code implementations6 May 2021 Shiyu Wang, Nicha C. Dvornek

Traditional regression models do not generalize well when learning from small and noisy datasets.

Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity

no code implementations15 Apr 2021 Nicha C. Dvornek, Xiaoxiao Li, Juntang Zhuang, Pamela Ventola, James S. Duncan

Heterogeneous presentation of a neurological disorder suggests potential differences in the underlying pathophysiological changes that occur in the brain.

Time Series

Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis

no code implementations29 Jul 2020 Xiaoxiao Li, Yuan Zhou, Nicha C. Dvornek, Muhan Zhang, Juntang Zhuang, Pamela Ventola, James S. Duncan

We propose an interpretable GNN framework with a novel salient region selection mechanism to determine neurological brain biomarkers associated with disorders.

Hepatocellular Carcinoma Intra-arterial Treatment Response Prediction for Improved Therapeutic Decision-Making

no code implementations1 Dec 2019 Junlin Yang, Nicha C. Dvornek, Fan Zhang, Julius Chapiro, MingDe Lin, Aaron Abajian, James S. Duncan

This work proposes a pipeline to predict treatment response to intra-arterial therapy of patients with Hepatocellular Carcinoma (HCC) for improved therapeutic decision-making.

Decision Making

Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI

no code implementations15 Oct 2019 Nicha C. Dvornek, Xiaoxiao Li, Juntang Zhuang, James S. Duncan

The addition of the generative model constrains the network to learn functional communities represented by the LSTM nodes that are both consistent with the data generation as well as useful for the classification task.

General Classification Time Series

Decision Explanation and Feature Importance for Invertible Networks

1 code implementation30 Sep 2019 Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li, Junlin Yang, James S. Duncan

We can determine the decision boundary of a linear classifier in the feature space; since the transform is invertible, we can invert the decision boundary from the feature space to the input space.

Feature Importance

Graph Embedding Using Infomax for ASD Classification and Brain Functional Difference Detection

no code implementations9 Aug 2019 Xiaoxiao Li, Nicha C. Dvornek, Juntang Zhuang, Pamela Ventola, James Duncan

Here, we model the whole brain fMRI as a graph, which preserves geometrical and temporal information and use a Graph Neural Network (GNN) to learn from the graph-structured fMRI data.

General Classification Graph Embedding

Invertible Network for Classification and Biomarker Selection for ASD

1 code implementation23 Jul 2019 Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li, Pamela Ventola, James S. Duncan

Recently deep learning methods have achieved success in the classification task of ASD using fMRI data.

General Classification

Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery

no code implementations14 Dec 2018 Xiaoxiao Li, Nicha C. Dvornek, Yuan Zhou, Juntang Zhuang, Pamela Ventola, James S. Duncan

Cooperative game theory is advantageous here because it directly considers the interaction between features and can be applied to any machine learning method, making it a novel, more accurate way of determining instance-wise biomarker importance from deep learning models.

Feature Importance

Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI

no code implementations23 Aug 2018 Xiaoxiao Li, Nicha C. Dvornek, Juntang Zhuang, Pamela Ventola, James S. Duncan

Therefore, in this work, we address the problem of interpreting reliable biomarkers associated with identifying ASD; specifically, we propose a 2-stage method that classifies ASD and control subjects using fMRI images and interprets the saliency features activated by the classifier.

Decision Making

Prediction of Autism Treatment Response from Baseline fMRI using Random Forests and Tree Bagging

no code implementations24 May 2018 Nicha C. Dvornek, Daniel Yang, Archana Venkataraman, Pamela Ventola, Lawrence H. Staib, Kevin A. Pelphrey, James S. Duncan

We propose predicting patient response to PRT from baseline task-based fMRI by the novel application of a random forest and tree bagging strategy.

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