Generative adversarial network in medical imaging: A review
Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …
community due to their capability of data generation without explicitly modelling the …
GANs for medical image analysis
Generative adversarial networks (GANs) and their extensions have carved open many
exciting ways to tackle well known and challenging medical image analysis problems such …
exciting ways to tackle well known and challenging medical image analysis problems such …
Medical image generation using generative adversarial networks: A review
Generative adversarial networks (GANs) are unsupervised deep learning approach in the
computer vision community which has gained significant attention from the last few years in …
computer vision community which has gained significant attention from the last few years in …
A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers
Brain signals refer to the biometric information collected from the human brain. The research
on brain signals aims to discover the underlying neurological or physical status of the …
on brain signals aims to discover the underlying neurological or physical status of the …
[PDF][PDF] A survey on deep learning based brain computer interface: Recent advances and new frontiers
Brain-Computer Interface (BCI) bridges human's neural world and the outer physical world
by decoding individuals' brain signals into commands recognizable by computer devices …
by decoding individuals' brain signals into commands recognizable by computer devices …
[HTML][HTML] Deep learning in the biomedical applications: Recent and future status
Deep neural networks represent, nowadays, the most effective machine learning technology
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …
Recurrent variational network: a deep learning inverse problem solver applied to the task of accelerated MRI reconstruction
Abstract Magnetic Resonance Imaging can produce detailed images of the anatomy and
physiology of the human body that can assist doctors in diagnosing and treating pathologies …
physiology of the human body that can assist doctors in diagnosing and treating pathologies …
Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity‐weighted coil combination
Purpose To systematically investigate the influence of various data consistency layers and
regularization networks with respect to variations in the training and test data domain, for …
regularization networks with respect to variations in the training and test data domain, for …
Reducing uncertainty in undersampled MRI reconstruction with active acquisition
The goal of MRI reconstruction is to restore a high fidelity image from partially observed
measurements. This partial view naturally induces reconstruction uncertainty that can only …
measurements. This partial view naturally induces reconstruction uncertainty that can only …
Deep learning for fast MR imaging: A review for learning reconstruction from incomplete k-space data
Magnetic resonance imaging is a powerful imaging modality that can provide versatile
information. However, it has a fundamental challenge that is time consuming to acquire …
information. However, it has a fundamental challenge that is time consuming to acquire …