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Found 11 papers, 2 papers with code
Unconditional Latent Diffusion Models Memorize Patient Imaging Data
Generative latent diffusion models hold a wide range of applications in the medical imaging domain.
Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesis
Despite the promise, the capacity of such models to memorize sensitive patient training data and synthesize samples showing high resemblance to training data samples is relatively unexplored.
BolT: Fused Window Transformers for fMRI Time Series Analysis
Encoding is performed on temporally-overlapped windows within the time series to capture local representations.
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.
A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networks
Methods: Neural networks were trained on thousands of samples from public datasets of either natural images or brain MR images.
