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09/08/2023
A Deep Learning Method for Sensitivity Enhancement of Deuterium Metabolic Imaging (DMI)
Purpose: Common to most MRSI techniques, the spatial resolution and the ...
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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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08/08/2023
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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04/06/2023
Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts
Integrating high-level semantically correlated contents and low-level an...
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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...
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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...
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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. ...
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02/18/2023
Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction
While enabling accelerated acquisition and improved reconstruction accur...
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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...
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02/03/2023
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
For medical image segmentation, contrastive learning is the dominant pra...
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09/27/2022
Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels
Recent studies on contrastive learning have achieved remarkable performa...
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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...
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06/06/2022
Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation
Contrastive learning has shown great promise over annotation scarcity pr...
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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...
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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...
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01/26/2022
Class-Aware Generative Adversarial Transformers for Medical Image Segmentation
Transformers have made remarkable progress towards modeling long-range d...
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10/11/2021
Momentum Centering and Asynchronous Update for Adaptive Gradient Methods
We propose ACProp (Asynchronous-centering-Prop), an adaptive optimizer w...
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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...
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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...
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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...
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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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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...
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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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10/15/2020
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
Most popular optimizers for deep learning can be broadly categorized as ...
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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...
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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...
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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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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,...
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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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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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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...
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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 ...
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05/24/2018