Transformers in medical imaging: A survey
Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …
successfully applied to several computer vision problems, achieving state-of-the-art results …
Intelligent metasurfaces: control, communication and computing
Controlling electromagnetic waves and information simultaneously by information
metasurfaces is of central importance in modern society. Intelligent metasurfaces are smart …
metasurfaces is of central importance in modern society. Intelligent metasurfaces are smart …
MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques
Background Detecting brain tumors in their early stages is crucial. Brain tumors are
classified by biopsy, which can only be performed through definitive brain surgery …
classified by biopsy, which can only be performed through definitive brain surgery …
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 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 …
Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks
Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease
cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR) …
cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR) …
Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing
Deep neural networks provide unprecedented performance gains in many real-world
problems in signal and image processing. Despite these gains, the future development and …
problems in signal and image processing. Despite these gains, the future development and …
Deep learning on image denoising: An overview
Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …
However, there are substantial differences in the various types of deep learning methods …
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 …
Sparse synthetic aperture radar imaging from compressed sensing and machine learning: Theories, applications, and trends
Synthetic aperture radar (SAR) image formation can be treated as a class of ill-posed linear
inverse problems, and the resolution is limited by the data bandwidth for traditional imaging …
inverse problems, and the resolution is limited by the data bandwidth for traditional imaging …