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Found 11 papers, 4 papers with code
GRJointNET: Synergistic Completion and Part Segmentation on 3D Incomplete Point Clouds
On the other hand, our proposed solution, GRJointNet, is an architecture that can perform joint completion and segmentation on point clouds as a successor of GRNet.
DDM$^2$: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models
Magnetic resonance imaging (MRI) is a common and life-saving medical imaging technique.
ResViT: Residual vision transformers for multi-modal medical image synthesis
Here, we propose a novel generative adversarial approach for medical image synthesis, ResViT, that leverages the contextual sensitivity of vision transformers along with the precision of convolution operators and realism of adversarial learning.}
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Image-to-Image Translation
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Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers
Supervised reconstruction models are characteristically trained on matched pairs of undersampled and fully-sampled data to capture an MRI prior, along with supervision regarding the imaging operator to enforce data consistency.
A Few-Shot Learning Approach for Accelerated MRI via Fusion of Data-Driven and Subject-Driven Priors
Deep neural networks (DNNs) have recently found emerging use in accelerated MRI reconstruction.
Three Dimensional MR Image Synthesis with Progressive Generative Adversarial Networks
Mainstream deep models for three-dimensional MRI synthesis are either cross-sectional or volumetric depending on the input.
Semi-Supervised Learning of Mutually Accelerated MRI Synthesis without Fully-Sampled Ground Truths
Here, we propose a novel semi-supervised deep generative model that instead learns to recover high-quality target images directly from accelerated acquisitions of source and target contrasts.
Progressively Volumetrized Deep Generative Models for Data-Efficient Contextual Learning of MR Image Recovery
Comprehensive demonstrations on mainstream MRI reconstruction and synthesis tasks show that ProvoGAN yields superior performance to state-of-the-art volumetric and cross-sectional models.
mustGAN: Multi-Stream Generative Adversarial Networks for MR Image Synthesis
Multi-contrast MRI protocols increase the level of morphological information available for diagnosis.
Synergistic Reconstruction and Synthesis via Generative Adversarial Networks for Accelerated Multi-Contrast MRI
The proposed method preserves high-frequency details of the target contrast by relying on the shared high-frequency information available from the source contrast, and prevents feature leakage or loss by relying on the undersampled acquisitions of the target contrast.
Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks
The proposed approach preserves high-frequency details via an adversarial loss; and it offers enhanced synthesis performance via a pixel-wise loss for registered multi-contrast images and a cycle-consistency loss for unregistered images.
