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08/29/2023
Learning Sequential Information in Task-based fMRI for Synthetic Data Augmentation
Insufficiency of training data is a persistent issue in medical image an...
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08/23/2023
TAI-GAN: Temporally and Anatomically Informed GAN for early-to-late frame conversion in dynamic cardiac PET motion correction
The rapid tracer kinetics of rubidium-82 (^82Rb) and high variation of c...
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08/08/2023
Copy Number Variation Informs fMRI-based Prediction of Autism Spectrum Disorder
The multifactorial etiology of autism spectrum disorder (ASD) suggests t...
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06/13/2022
Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network
Subject motion in whole-body dynamic PET introduces inter-frame mismatch...
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05/06/2021
A Metamodel Structure For Regression Analysis: Application To Prediction Of Autism Spectrum Disorder Severity
Traditional regression models do not generalize well when learning from ...
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05/06/2021
Estimating Reproducible Functional Networks Associated with Task Dynamics using Unsupervised LSTMs
We propose a method for estimating more reproducible functional networks...
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04/15/2021
Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity
Heterogeneous presentation of a neurological disorder suggests potential...
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02/09/2021
MALI: A memory efficient and reverse accurate integrator for Neural ODEs
Neural ordinary differential equations (Neural ODEs) are a new family of...
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07/29/2020
Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis
Understanding how certain brain regions relate to a specific neurologica...
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12/01/2019
Hepatocellular Carcinoma Intra-arterial Treatment Response Prediction for Improved Therapeutic Decision-Making
This work proposes a pipeline to predict treatment response to intra-art...
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10/15/2019
Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI
Recurrent neural networks (RNNs) were designed for dealing with time-ser...
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09/30/2019
Decision Explanation and Feature Importance for Invertible Networks
Deep neural networks are vulnerable to adversarial attacks and hard to i...
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08/27/2019
Domain-Agnostic Learning with Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation
Domain Adaptation (DA) has the potential to greatly help the generalizat...
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08/09/2019
Graph Embedding Using Infomax for ASD Classification and Brain Functional Difference Detection
Significant progress has been made using fMRI to characterize the brain ...
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07/31/2019
Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation
A deep learning model trained on some labeled data from a certain source...
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07/23/2019
Invertible Network for Classification and Biomarker Selection for ASD
Determining biomarkers for autism spectrum disorder (ASD) is crucial to ...
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07/02/2019
Graph Neural Network for Interpreting Task-fMRI Biomarkers
Finding the biomarkers associated with ASD is helpful for understanding ...
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12/14/2018
Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery
Discovering imaging biomarkers for autism spectrum disorder (ASD) is cri...
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10/28/2018
Prediction of severity and treatment outcome for ASD from fMRI
Autism spectrum disorder (ASD) is a complex neurodevelopmental syndrome....
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10/17/2018
Prediction of treatment outcome for autism from structure of the brain based on sure independence screening
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder,...
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08/23/2018
Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder....
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05/24/2018