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 …
A review on medical imaging synthesis using deep learning and its clinical applications
This paper reviewed the deep learning‐based studies for medical imaging synthesis and its
clinical application. Specifically, we summarized the recent developments of deep learning …
clinical application. Specifically, we summarized the recent developments of deep learning …
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 …
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 …
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 …
[HTML][HTML] An overview of deep learning in medical imaging focusing on MRI
AS Lundervold, A Lundervold - Zeitschrift für Medizinische Physik, 2019 - Elsevier
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 …
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 …
ADMM-CSNet: A deep learning approach for image compressive sensing
Compressive sensing (CS) is an effective technique for reconstructing image from a small
amount of sampled data. It has been widely applied in medical imaging, remote sensing …
amount of sampled data. It has been widely applied in medical imaging, remote sensing …
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 …
Accelerated MR Image Reconstruction Using Machine Learning | Radiology: Artificial …
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 …