The roles of supervised machine learning in systems neuroscience
Over the last several years, the use of machine learning (ML) in neuroscience has been
rapidly increasing. Here, we review ML's contributions, both realized and potential, across …
rapidly increasing. Here, we review ML's contributions, both realized and potential, across …
Clinical assessment of deep learning–based super-resolution for 3D volumetric brain MRI
JD Rudie, T Gleason, MJ Barkovich… - Radiology: Artificial …, 2022 - pubs.rsna.org
Artificial intelligence (AI)–based image enhancement has the potential to reduce scan times
while improving signal-to-noise ratio (SNR) and maintaining spatial resolution. This study …
while improving signal-to-noise ratio (SNR) and maintaining spatial resolution. This study …
Deep learning for ultrasound localization microscopy
X Liu, T Zhou, M Lu, Y Yang, Q He… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
By localizing microbubbles (MBs) in the vasculature, ultrasound localization microscopy
(ULM) has recently been proposed, which greatly improves the spatial resolution of …
(ULM) has recently been proposed, which greatly improves the spatial resolution of …
Deep neural network-based sinogram super-resolution and bandwidth enhancement for limited-data photoacoustic tomography
Photoacoustic tomography (PAT) is a noninvasive imaging modality combining the benefits
of optical contrast at ultrasonic resolution. Analytical reconstruction algorithms for …
of optical contrast at ultrasonic resolution. Analytical reconstruction algorithms for …
Actor neural networks for the robust control of partially measured nonlinear systems showcased for image propagation through diffuse media
The output of physical systems, such as the scrambled pattern formed by shining the spot of
a laser pointer through fog, is often easily accessible by direct measurements. However …
a laser pointer through fog, is often easily accessible by direct measurements. However …
DeepQSM-using deep learning to solve the dipole inversion for quantitative susceptibility mapping
S Bollmann, KGB Rasmussen, M Kristensen… - Neuroimage, 2019 - Elsevier
Quantitative susceptibility mapping (QSM) is based on magnetic resonance imaging (MRI)
phase measurements and has gained broad interest because it yields relevant information …
phase measurements and has gained broad interest because it yields relevant information …
Machine learning in electromagnetics with applications to biomedical imaging: A review
Biomedical imaging is a relevant noninvasive technique aimed at generating an image of
the biological structure under analysis. The arising visual representation of the …
the biological structure under analysis. The arising visual representation of the …
CryoGAN: A new reconstruction paradigm for single-particle cryo-EM via deep adversarial learning
We present CryoGAN, a new paradigm for single-particle cryo-electron microscopy (cryo-
EM) reconstruction based on unsupervised deep adversarial learning. In single-particle cryo …
EM) reconstruction based on unsupervised deep adversarial learning. In single-particle cryo …
Scalable plug-and-play ADMM with convergence guarantees
Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse
problems by exploiting statistical priors specified as denoisers. Recent work has reported …
problems by exploiting statistical priors specified as denoisers. Recent work has reported …
Deep learning methods for solving linear inverse problems: Research directions and paradigms
The linear inverse problem is fundamental to the development of various scientific areas.
Innumerable attempts have been carried out to solve different variants of the linear inverse …
Innumerable attempts have been carried out to solve different variants of the linear inverse …