James S. Duncan

research

∙ 04/06/2023

Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts

Integrating high-level semantically correlated contents and low-level an...

0 Chenyu You, et al. ∙

research

∙ 04/05/2023

ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast

Medical data often exhibits long-tail distributions with heavy class imb...

0 Chenyu You, et al. ∙

research

∙ 04/02/2023

FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising

Low-count PET is an efficient way to reduce radiation exposure and acqui...

0 Bo Zhou, et al. ∙

research

∙ 03/02/2023

Meta-information-aware Dual-path Transformer for Differential Diagnosis of Multi-type Pancreatic Lesions in Multi-phase CT

Pancreatic cancer is one of the leading causes of cancer-related death. ...

0 Bo Zhou, et al. ∙

research

∙ 02/18/2023

Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction

While enabling accelerated acquisition and improved reconstruction accur...

0 Bo Zhou, et al. ∙

research

∙ 02/14/2023

Fast-MC-PET: A Novel Deep Learning-aided Motion Correction and Reconstruction Framework for Accelerated PET

Patient motion during PET is inevitable. Its long acquisition time not o...

0 Bo Zhou, et al. ∙

research

∙ 02/03/2023

Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective

For medical image segmentation, contrastive learning is the dominant pra...

0 Chenyu You, et al. ∙

research

∙ 09/27/2022

Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels

Recent studies on contrastive learning have achieved remarkable performa...

0 Chenyu You, et al. ∙

research

∙ 07/20/2022

Flow-based Visual Quality Enhancer for Super-resolution Magnetic Resonance Spectroscopic Imaging

Magnetic Resonance Spectroscopic Imaging (MRSI) is an essential tool for...

0 Siyuan Dong, et al. ∙

research

∙ 06/06/2022

Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation

Contrastive learning has shown great promise over annotation scarcity pr...

0 Chenyu You, et al. ∙

research

∙ 06/03/2022

Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation

Many medical datasets have recently been created for medical image segme...

0 Chenyu You, et al. ∙

research

∙ 01/26/2022

DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction

Multi-contrast MRI (MC-MRI) captures multiple complementary imaging moda...

0 Bo Zhou, et al. ∙

research

∙ 01/26/2022

Class-Aware Generative Adversarial Transformers for Medical Image Segmentation

Transformers have made remarkable progress towards modeling long-range d...

0 Chenyu You, et al. ∙

research

∙ 10/11/2021

Momentum Centering and Asynchronous Update for Adaptive Gradient Methods

We propose ACProp (Asynchronous-centering-Prop), an adaptive optimizer w...

0 Juntang Zhuang, et al. ∙

research

∙ 08/13/2021

SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation

Automated segmentation in medical image analysis is a challenging task t...

10 Chenyu You, et al. ∙

research

∙ 07/12/2021

Anatomy-Constrained Contrastive Learning for Synthetic Segmentation without Ground-truth

A large amount of manual segmentation is typically required to train a r...

0 Bo Zhou, et al. ∙

research

∙ 05/14/2021

Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation

Automated segmentation in medical image analysis is a challenging task t...

0 Chenyu You, et al. ∙

research

∙ 05/06/2021

Estimating Reproducible Functional Networks Associated with Task Dynamics using Unsupervised LSTMs

We propose a method for estimating more reproducible functional networks...

0 Nicha C. Dvornek, et al. ∙

research

∙ 04/15/2021

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

Heterogeneous presentation of a neurological disorder suggests potential...

14 Nicha C. Dvornek, et al. ∙

research

∙ 04/14/2021

Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration

Multimodal image registration has many applications in diagnostic medica...

0 Bo Zhou, et al. ∙

research

∙ 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...

0 Juntang Zhuang, et al. ∙

research

∙ 10/15/2020

AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients

Most popular optimizers for deep learning can be broadly categorized as ...

48 Juntang Zhuang, et al. ∙

research

∙ 09/06/2020

Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation

Deep neural networks have shown exceptional learning capability and gene...

8 Chenyu You, et al. ∙

research

∙ 09/03/2020

Limited View Tomographic Reconstruction Using a Deep Recurrent Framework with Residual Dense Spatial-Channel Attention Network and Sinogram Consistency

Limited view tomographic reconstruction aims to reconstruct a tomographi...

0 Bo Zhou, et al. ∙

research

∙ 07/29/2020

Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis

Understanding how certain brain regions relate to a specific neurologica...

26 Xiaoxiao Li, et al. ∙

research

∙ 01/16/2020

Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results

Deep learning models have shown their advantage in many different tasks,...

0 Xiaoxiao Li, et al. ∙

research

∙ 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...

0 Junlin Yang, et al. ∙

research

∙ 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...

0 Nicha C. Dvornek, et al. ∙

research

∙ 09/30/2019

Decision Explanation and Feature Importance for Invertible Networks

Deep neural networks are vulnerable to adversarial attacks and hard to i...

0 Juntang Zhuang, et al. ∙

research

∙ 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...

11 Junlin Yang, et al. ∙

research

∙ 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...

2 Junlin Yang, et al. ∙

research

∙ 07/23/2019

Invertible Network for Classification and Biomarker Selection for ASD

Determining biomarkers for autism spectrum disorder (ASD) is crucial to ...

0 Juntang Zhuang, et al. ∙

research

∙ 07/02/2019

Graph Neural Network for Interpreting Task-fMRI Biomarkers

Finding the biomarkers associated with ASD is helpful for understanding ...

0 Xiaoxiao Li, et al. ∙

research

∙ 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...

0 Xiaoxiao Li, et al. ∙

research

∙ 10/28/2018

Prediction of severity and treatment outcome for ASD from fMRI

Autism spectrum disorder (ASD) is a complex neurodevelopmental syndrome....

0 Juntang Zhuang, et al. ∙

research

∙ 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,...

0 Juntang Zhuang, et al. ∙

research

∙ 08/23/2018

Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder....

4 Xiaoxiao Li, et al. ∙

research

∙ 07/12/2018

Learning-based Regularization for Cardiac Strain Analysis with Ability for Domain Adaptation

Reliable motion estimation and strain analysis using 3D+time echocardiog...

6 Allen Lu, et al. ∙

research

∙ 07/09/2018

Flow Network Tracking for Spatiotemporal and Periodic Point Matching: Applied to Cardiac Motion Analysis

The accurate quantification of left ventricular (LV) deformation/strain ...

2 Nripesh Parajuli, et al. ∙

research

∙ 05/24/2018

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

Treating children with autism spectrum disorders (ASD) with behavioral i...

0 Nicha C. Dvornek, et al. ∙

James S. Duncan

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