Federated learning for medical image analysis: A survey
Abstract Machine learning in medical imaging often faces a fundamental dilemma, namely,
the small sample size problem. Many recent studies suggest using multi-domain data …
the small sample size problem. Many recent studies suggest using multi-domain data …
Unsupervised deep learning methods for biological image reconstruction and enhancement: An overview from a signal processing perspective
Recently, deep learning (DL) approaches have become the main research frontier for
biological image reconstruction and enhancement problems thanks to their high …
biological image reconstruction and enhancement problems thanks to their high …
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 equilibrium architectures for inverse problems in imaging
Recent efforts on solving inverse problems in imaging via deep neural networks use
architectures inspired by a fixed number of iterations of an optimization method. The number …
architectures inspired by a fixed number of iterations of an optimization method. The number …
The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and Smale's 18th problem
MJ Colbrook, V Antun… - Proceedings of the …, 2022 - National Acad Sciences
Deep learning (DL) has had unprecedented success and is now entering scientific
computing with full force. However, current DL methods typically suffer from instability, even …
computing with full force. However, current DL methods typically suffer from instability, even …
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 …
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging
Physics-driven deep learning methods have emerged as a powerful tool for computational
magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new …
magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new …
B-spline parameterized joint optimization of reconstruction and k-space trajectories (bjork) for accelerated 2d mri
Optimizing k-space sampling trajectories is a promising yet challenging topic for fast
magnetic resonance imaging (MRI). This work proposes to optimize a reconstruction method …
magnetic resonance imaging (MRI). This work proposes to optimize a reconstruction method …
Humus-net: Hybrid unrolled multi-scale network architecture for accelerated mri reconstruction
In accelerated MRI reconstruction, the anatomy of a patient is recovered from a set of
undersampled and noisy measurements. Deep learning approaches have been proven to …
undersampled and noisy measurements. Deep learning approaches have been proven to …
Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the autoimplant 2021 cranial implant design challenge
Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects.
These implants are usually generated offline and may require days to weeks to be available …
These implants are usually generated offline and may require days to weeks to be available …