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Found 28 papers, 15 papers with code
HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images
While CNNs were previously augmented with attention maps or spatial masks to guide focus on potentially critical regions, learning localization guidance under heterogeneity in the spatial distribution of pathology is challenging.
CalibFPA: A Focal Plane Array Imaging System based on Online Deep-Learning Calibration
In this study, we propose a novel compressive FPA system based on online deep-learning calibration of multiplexed LR measurements (CalibFPA).
Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction
Instead of the target transformation from undersampled to fully-sampled data required for MRI reconstruction, common diffusion priors are trained to learn a task-agnostic transformation from an asymptotic start-point of Gaussian noise onto the finite end-point of fully-sampled data.
DreaMR: Diffusion-driven Counterfactual Explanation for Functional MRI
Deep learning analyses have offered sensitivity leaps in detection of cognitive states from functional MRI (fMRI) measurements across the brain.
FD-Net: An Unsupervised Deep Forward-Distortion Model for Susceptibility Artifact Correction in EPI
Unlike previous methods, FD-Net enforces the forward-distortions of the correct image in both PE directions to be consistent with the acquired reversed-PE image pair.
Learning Deep MRI Reconstruction Models from Scratch in Low-Data Regimes
To alleviate error propagation, PSFNet combines its SS and SG priors via a novel parallel-stream architecture with learnable fusion parameters.
A plug-in graph neural network to boost temporal sensitivity in fMRI analysis
Receiving brain regions as nodes and blood-oxygen-level-dependent (BOLD) signals as node inputs, the proposed GraphCorr method leverages a node embedder module based on a transformer encoder to capture temporally-windowed latent representations of BOLD signals.
DEQ-MPI: A Deep Equilibrium Reconstruction with Learned Consistency for Magnetic Particle Imaging
The ill-posed reconstruction problem can be solved by simultaneously enforcing data consistency based on the SM and regularizing the solution based on an image prior.
Unsupervised Simplification of Legal Texts
While a recent study proposed a rule-based TS method for legal text, learning-based TS in the legal domain has not been considered previously.
Unsupervised Medical Image Translation with Adversarial Diffusion Models
Extensive assessments are reported on the utility of SynDiff against competing GAN and diffusion models in multi-contrast MRI and MRI-CT translation.
Ranked #7 on
Image-to-Image Translation
on IXI
One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis
As such, pFLSynth enables training of a unified synthesis model that can reliably generalize across multiple sites and translation tasks.
Adaptive Diffusion Priors for Accelerated MRI Reconstruction
A two-phase reconstruction is executed following training: a rapid-diffusion phase that produces an initial reconstruction with the trained prior, and an adaptation phase that further refines the result by updating the prior to minimize data-consistency loss.
BolT: Fused Window Transformers for fMRI Time Series Analysis
Encoding is performed on temporally-overlapped windows within the time series to capture local representations.
Federated Learning of Generative Image Priors for MRI Reconstruction
Specificity in the prior is preserved via a mapper subnetwork that produces site-specific latents.
TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging
Magnetic particle imaging (MPI) offers exceptional contrast for magnetic nanoparticles (MNP) at high spatio-temporal resolution.
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.}
Ranked #1 on
Image-to-Image Translation
on IXI
Constrained Ellipse Fitting for Efficient Parameter Mapping with Phase-cycled bSSFP MRI
CELF is based on the elliptical signal model framework for complex bSSFP signals; and it introduces geometrical constraints on ellipse properties to improve estimation efficiency, and dictionary-based identification to improve estimation accuracy.
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.
Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI
Compressed sensing applied to magnetic resonance imaging (MRI) allows to reduce the scanning time by enabling images to be reconstructed from highly undersampled data.
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.
Learning-Based Compressive MRI
In the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms have been proposed that can be used with general Fourier subsampling patterns.
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.
