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Found 9 papers, 0 papers with code
scHyena: Foundation Model for Full-Length Single-Cell RNA-Seq Analysis in Brain
Single-cell RNA sequencing (scRNA-seq) has made significant strides in unraveling the intricate cellular diversity within complex tissues.
Unpaired Deep Learning for Pharmacokinetic Parameter Estimation from Dynamic Contrast-Enhanced MRI
DCE-MRI provides information about vascular permeability and tissue perfusion through the acquisition of pharmacokinetic parameters.
Annealed Score-Based Diffusion Model for MR Motion Artifact Reduction
Motion artifact reduction is one of the important research topics in MR imaging, as the motion artifact degrades image quality and makes diagnosis difficult.
CycleQSM: Unsupervised QSM Deep Learning using Physics-Informed CycleGAN
In contrast to the conventional cycleGAN, our novel cycleGAN has only one generator and one discriminator thanks to the known dipole kernel.
Unsupervised MR Motion Artifact Deep Learning using Outlier-Rejecting Bootstrap Aggregation
Recently, deep learning approaches for MR motion artifact correction have been extensively studied.
Unpaired Deep Learning for Accelerated MRI using Optimal Transport Driven CycleGAN
Recently, deep learning approaches for accelerated MRI have been extensively studied thanks to their high performance reconstruction in spite of significantly reduced runtime complexity.
Geometric Approaches to Increase the Expressivity of Deep Neural Networks for MR Reconstruction
Recently, it was shown that an encoder-decoder convolutional neural network (CNN) can be interpreted as a piecewise linear basis-like representation, whose specific representation is determined by the ReLU activation patterns for a given input image.
Optimal Transport driven CycleGAN for Unsupervised Learning in Inverse Problems
To improve the performance of classical generative adversarial network (GAN), Wasserstein generative adversarial networks (W-GAN) was developed as a Kantorovich dual formulation of the optimal transport (OT) problem using Wasserstein-1 distance.
OPTIMAL TRANSPORT, CYCLEGAN, AND PENALIZED LS FOR UNSUPERVISED LEARNING IN INVERSE PROBLEMS
Specifically, we reveal that a cycleGAN architecture can be derived as a dual formulation of the optimal transport problem, if the PLS with a deep learning penalty is used as a transport cost between the two probability measures from measurements and unknown images.
