
Geometry Constrained Weakly Supervised Object Localization
We propose a geometry constrained network, termed GCNet, for weakly sup...
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Deep Network Interpolation for Accelerated Parallel MR Image Reconstruction
We present a deep network interpolation strategy for accelerated paralle...
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G2LNet: Global to Local Network for Realtime 6D Pose Estimation with Embedding Vector Features
In this paper, we propose a novel realtime 6D object pose estimation fr...
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Σnet: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction
Purpose: To systematically investigate the influence of various data con...
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Σnet: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction
We explore an ensembled Σnet for fast parallel MR imaging, including pa...
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Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image...
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Data consistency networks for (calibrationless) accelerated parallel MR image reconstruction
We present simple reconstruction networks for multicoil data by extendi...
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dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance
AUTOMAP is a promising generalized reconstruction approach, however, it ...
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kt NEXT: Dynamic MR Image Reconstruction Exploiting Spatiotemporal Correlations
Dynamic magnetic resonance imaging (MRI) exhibits high correlations in k...
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VSNet: Variable splitting network for accelerated parallel MRI reconstruction
In this work, we propose a deep learning approach for parallel magnetic ...
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Data Efficient Unsupervised Domain Adaptation for CrossModality Image Segmentation
Deep learning models trained on medical images from a source domain (e.g...
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SelfSupervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction
In the recent years, convolutional neural networks have transformed the ...
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Explainable Shape Analysis through Deep Hierarchical Generative Models: Application to Cardiac Remodeling
Quantification of anatomical shape changes still relies on scalar global...
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A new nonlocal forward model for diffuse optical tomography
The forward model in diffuse optical tomography (DOT) describes how ligh...
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Graph and finite elementbased total variation models for the inverse problem in diffuse optical tomography
Total variation (TV) is a powerful regularization method that has been w...
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Deep learning cardiac motion analysis for human survival prediction
Motion analysis is used in computer vision to understand the behaviour o...
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Automatic 3D biventricular segmentation of cardiac images by a shapeconstrained multitask deep learning approach
Deep learning approaches have achieved stateoftheart performance in c...
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OCT segmentation: Integrating open parametric contour model of the retinal layers and shape constraint to the MumfordShah functional
In this paper, we propose a novel retinal layer boundary model for segme...
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Deep nested level sets: Fully automated segmentation of cardiac MR images in patients with pulmonary hypertension
In this paper we introduce a novel and accurate optimisation method for ...
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Tensor Based Second Order Variational Model for Image Reconstruction
Second order total variation (SOTV) models have advantages for image rec...
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Automated Segmentation of Retinal Layers from Optical Coherent Tomography Images Using Geodesic Distance
Optical coherence tomography (OCT) is a noninvasive imaging technique t...
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Jinming Duan
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