Deep learning for tomographic image reconstruction
Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …
intelligence and a new frontier of machine learning. Deep learning has been widely used in …
Deep learning in medical imaging and radiation therapy
The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …
therapy are to (a) summarize what has been achieved to date;(b) identify common and …
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 techniques for inverse problems in imaging
Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …
wide variety of inverse problems arising in computational imaging. We explore the central …
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
Surrogate modeling and uncertainty quantification tasks for PDE systems are most often
considered as supervised learning problems where input and output data pairs are used for …
considered as supervised learning problems where input and output data pairs are used for …
On instabilities of deep learning in image reconstruction and the potential costs of AI
Deep learning, due to its unprecedented success in tasks such as image classification, has
emerged as a new tool in image reconstruction with potential to change the field. In this …
emerged as a new tool in image reconstruction with potential to change the field. In this …
fastMRI: An open dataset and benchmarks for accelerated MRI
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the
potential to reduce medical costs, minimize stress to patients and make MRI possible in …
potential to reduce medical costs, minimize stress to patients and make MRI possible in …
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 …