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07/06/2023
Fourier-Net+: Leveraging Band-Limited Representation for Efficient 3D Medical Image Registration
U-Net style networks are commonly utilized in unsupervised image registr...
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03/22/2023
Dense-Localizing Audio-Visual Events in Untrimmed Videos: A Large-Scale Benchmark and Baseline
Existing audio-visual event localization (AVE) handles manually trimmed ...
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11/29/2022
Fourier-Net: Fast Image Registration with Band-limited Deformation
Unsupervised image registration commonly adopts U-Net style networks to ...
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08/07/2022
U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration?
Due to their extreme long-range modeling capability, vision transformer-...
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05/25/2022
Structure Unbiased Adversarial Model for Medical Image Segmentation
Generative models have been widely proposed in image recognition to gene...
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10/06/2021
Accelerated First Order Methods for Variational Imaging
In this thesis, we offer a thorough investigation of different regularis...
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09/26/2021
Nesterov Accelerated ADMM for Fast Diffeomorphic Image Registration
Deterministic approaches using iterative optimisation have been historic...
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05/25/2021
Learning a Model-Driven Variational Network for Deformable Image Registration
Data-driven deep learning approaches to image registration can be less a...
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03/12/2021
FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism
In this paper, we focus on category-level 6D pose and size estimation fr...
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07/19/2020
Geometry Constrained Weakly Supervised Object Localization
We propose a geometry constrained network, termed GC-Net, for weakly sup...
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07/12/2020
Deep Network Interpolation for Accelerated Parallel MR Image Reconstruction
We present a deep network interpolation strategy for accelerated paralle...
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03/24/2020
G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features
In this paper, we propose a novel real-time 6D object pose estimation fr...
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12/18/2019
Σ-net: 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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12/11/2019
Σ-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction
We explore an ensembled Σ-net for fast parallel MR imaging, including pa...
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11/09/2019
Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image...
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09/25/2019
Data consistency networks for (calibration-less) accelerated parallel MR image reconstruction
We present simple reconstruction networks for multi-coil data by extendi...
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09/24/2019
dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance
AUTOMAP is a promising generalized reconstruction approach, however, it ...
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07/22/2019
k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlations
Dynamic magnetic resonance imaging (MRI) exhibits high correlations in k...
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07/19/2019
VS-Net: Variable splitting network for accelerated parallel MRI reconstruction
In this work, we propose a deep learning approach for parallel magnetic ...
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07/05/2019
Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation
Deep learning models trained on medical images from a source domain (e.g...
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07/05/2019
Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction
In the recent years, convolutional neural networks have transformed the ...
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06/28/2019
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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06/03/2019
A new nonlocal forward model for diffuse optical tomography
The forward model in diffuse optical tomography (DOT) describes how ligh...
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01/07/2019
Graph- and finite element-based 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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10/08/2018
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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08/26/2018
Automatic 3D bi-ventricular segmentation of cardiac images by a shape-constrained multi-task deep learning approach
Deep learning approaches have achieved state-of-the-art performance in c...
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08/08/2018
OCT segmentation: Integrating open parametric contour model of the retinal layers and shape constraint to the Mumford-Shah functional
In this paper, we propose a novel retinal layer boundary model for segme...
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07/27/2018
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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09/27/2016
Tensor Based Second Order Variational Model for Image Reconstruction
Second order total variation (SOTV) models have advantages for image rec...
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09/07/2016