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A review on generative adversarial networks: Algorithms, theory, and applications
Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …
however, they have been studied since 2014, and a large number of algorithms have been …
An overview of deep learning in medical imaging focusing on MRI
What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …
medical image analysis? Machine learning has witnessed a tremendous amount of attention …
Adaptive diffusion priors for accelerated MRI reconstruction
Deep MRI reconstruction is commonly performed with conditional models that de-alias
undersampled acquisitions to recover images consistent with fully-sampled data. Since …
undersampled acquisitions to recover images consistent with fully-sampled data. Since …
Robust compressed sensing mri with deep generative priors
Abstract The CSGM framework (Bora-Jalal-Price-Dimakis' 17) has shown that
deepgenerative priors can be powerful tools for solving inverse problems. However, to date …
deepgenerative priors can be powerful tools for solving inverse problems. However, to date …
Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations
Fault detection and diagnosis of gear systems using vibration measurements play an
important role in ensuring their functional reliability and safety. Computational intelligence …
important role in ensuring their functional reliability and safety. Computational intelligence …
NeRP: implicit neural representation learning with prior embedding for sparsely sampled image reconstruction
Image reconstruction is an inverse problem that solves for a computational image based on
sampled sensor measurement. Sparsely sampled image reconstruction poses additional …
sampled sensor measurement. Sparsely sampled image reconstruction poses additional …
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 …
undersampled and fully-sampled data to capture an MRI prior, along with supervision …
Deep learning for multigrade brain tumor classification in smart healthcare systems: A prospective survey
Brain tumor is one of the most dangerous cancers in people of all ages, and its grade
recognition is a challenging problem for radiologists in health monitoring and automated …
recognition is a challenging problem for radiologists in health monitoring and automated …
fastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning
fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated
MR Image Reconstruction Using Machine Learning | Radiology: Artificial Intelligence RSNA …
MR Image Reconstruction Using Machine Learning | Radiology: Artificial Intelligence RSNA …
Fastsurfer-a fast and accurate deep learning based neuroimaging pipeline
Traditional neuroimage analysis pipelines involve computationally intensive, time-
consuming optimization steps, and thus, do not scale well to large cohort studies with …
consuming optimization steps, and thus, do not scale well to large cohort studies with …