Dinggang Shen onderzoeker
Shen, Dinggang.
VIAF ID: 175325154 (Personal)
Permalink: http://viaf.org/viaf/175325154
Preferred Forms
- 100 0 _ ‡a Dinggang Shen ‡c onderzoeker
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- 100 1 _ ‡a Shen, Dinggang
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- 100 1 _ ‡a Shen, Dinggang
- 100 1 _ ‡a Shen, Dinggang
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4xx's: Alternate Name Forms (3)
5xx's: Related Names (1)
Works
Title | Sources |
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Deep learning-based feature representation for AD/MCI classification | |
Deep learning based imaging data completion for improved brain disease diagnosis | |
Deep Learning Deformation Initialization for Rapid Groupwise Registration of Inhomogeneous Image Populations | |
Deep Learning for Fast and Spatially-Constrained Tissue Quantification from Highly-Undersampled Data in Magnetic Resonance Fingerprinting (MRF) | |
Deep learning for medical image analysis | |
Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients | |
Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection | |
A deep learning system for detecting diabetic retinopathy across the disease spectrum | |
Deep Multi-Scale Mesh Feature Learning for Automated Labeling of Raw Dental Surfaces from 3D Intraoral Scanners | |
Deep Multi-Task Multi-Channel Learning for Joint Classification and Regression of Brain Status | |
Deformable registration of cortical structures via hybrid volumetric and surface warping. | |
Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method. | |
Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning | |
Denoising Diffusion-Weighted Images Using Grouped Iterative Hard Thresholding of Multi-Channel Framelets | |
Denoising Magnetic Resonance Images Using Collaborative Non-Local Means | |
Denoising of Diffusion MRI Data via Graph Framelet Matching in x-q Space | |
Designing single- and multiple-shell sampling schemes for diffusion MRI using spherical code | |
Detail-preserving construction of neonatal brain atlases in space-frequency domain. | |
Detecting Anatomical Landmarks for Fast Alzheimer's Disease Diagnosis | |
Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks | |
Detecting Cognitive States from fMRI Images by Machine Learning and Multivariate Classification | |
Detection of Arterial Calcification in Mammograms by Random Walks | |
Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging | |
Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels. | |
Developing Novel Weighted Correlation Kernels for Convolutional Neural Networks to Extract Hierarchical Functional Connectivities from fMRI for Disease Diagnosis | |
Development of cortical anatomical properties from early childhood to early adulthood | |
Development of Dynamic Functional Architecture during Early Infancy | |
Development trends of white matter connectivity in the first years of life | |
Developmental Patterns Based Individualized Parcellation of Infant Cortical Surface | |
Developmental topography of cortical thickness during infancy | |
Diagnosis of autism spectrum disorders using regional and interregional morphological features | |
Diagnosis of brain abnormality using both structural and functional MR images. | |
Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning | |
DICCCOL: dense individualized and common connectivity-based cortical landmarks | |
Difficulty-aware hierarchical convolutional neural networks for deformable registration of brain MR images | |
Diffusion Compartmentalization Using Response Function Groups with Cardinality Penalization | |
Diffusion tensor image registration with combined tract and tensor features | |
Diffusion tensor imaging based network analysis detects alterations of neuroconnectivity in patients with clinically early relapsing-remitting multiple sclerosis | |
DIKA-Nets: Domain-Invariant Knowledge-guided Attention Networks for Brain Skull Stripping of Early Developing Macaques | |
Dilated Dense U-Net for Infant Hippocampus Subfield Segmentation | |
Directed graph based image registration | |
Discovering cortical sulcal folding patterns in neonates using large-scale dataset. | |
Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features | |
Discriminative multi-task feature selection for multi-modality classification of Alzheimer's disease | |
Discriminative self-representation sparse regression for neuroimaging-based alzheimer's disease diagnosis. | |
Disease-Image-Specific Learning for Diagnosis-Oriented Neuroimage Synthesis With Incomplete Multi-Modality Data | |
A dynamic tree-based registration could handle possible large deformations among MR brain images | |
An Effective MR-Guided CT Network Training for Segmenting Prostate in CT Images | |
De-enhancing the dynamic contrast-enhanced breast MRI for robust registration | |
A feature-based learning framework for accurate prostate localization in CT images. | |
A framework for predictive modeling of anatomical deformations | |
A general fast registration framework by learning deformation-appearance correlation | |
A generative model for resolution enhancement of diffusion MRI data | |
A generative probability model of joint label fusion for multi-atlas based brain segmentation | |
Information Processing in Medical Imaging : 25th International Conference, IPMI 2017, Boone, NC, USA, June 25-30, 2017, Proceedings | |
Integration of sparse multi-modality representation and geometrical constraint for isointense infant brain segmentation | |
Machine learning in medical imaging, [2012]: | |
Medical image computing and computer assisted intervention -- MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings. | |
Medical Imaging and Augmented Reality : Third International Workshop, Shanghai, China, August 17-18, 2006, Proceedings | |
Multimodal Brain Image Analysis : Third International Workshop, MBIA 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013, Proceedings | |
Nonlocal atlas-guided multi-channel forest learning for human brain labeling | |
A novel approach to multiple anatomical shape analysis: Application to fetal ventriculomegaly | |
A novel relational regularization feature selection method for joint regression and classification in AD diagnosis | |
A Point Says a Lot: An Interactive Segmentation Method for MR Prostate via One-Point Labeling | |
A Robust Deep Model for Improved Classification of AD/MCI Patients | |
A Statistical Atlas of Prostate Cancer for Optimal Biopsy | |
A statistical framework for inter-group image registration. | |
A toolbox for brain network construction and classification (BrainNetClass) | |
A transversal approach for patch-based label fusion via matrix completion |