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 …
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 …
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 …
[HTML][HTML] The promise of artificial intelligence and deep learning in PET and SPECT imaging
This review sets out to discuss the foremost applications of artificial intelligence (AI),
particularly deep learning (DL) algorithms, in single-photon emission computed tomography …
particularly deep learning (DL) algorithms, in single-photon emission computed tomography …
Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning
Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled
data is important in many clinical applications. In recent years, deep learning-based …
data is important in many clinical applications. In recent years, deep learning-based …
Deep learning for PET image reconstruction
This article reviews the use of a subdiscipline of artificial intelligence (AI), deep learning, for
the reconstruction of images in positron emission tomography (PET). Deep learning can be …
the reconstruction of images in positron emission tomography (PET). Deep learning can be …
SNIPS: Solving noisy inverse problems stochastically
In this work we introduce a novel stochastic algorithm dubbed SNIPS, which draws samples
from the posterior distribution of any linear inverse problem, where the observation is …
from the posterior distribution of any linear inverse problem, where the observation is …
Making radiomics more reproducible across scanner and imaging protocol variations: a review of harmonization methods
Radiomics converts medical images into mineable data via a high-throughput extraction of
quantitative features used for clinical decision support. However, these radiomic features are …
quantitative features used for clinical decision support. However, these radiomic features are …
Light-sheets and smart microscopy, an exciting future is dawning
S Daetwyler, RP Fiolka - Communications biology, 2023 - nature.com
Light-sheet fluorescence microscopy has transformed our ability to visualize and
quantitatively measure biological processes rapidly and over long time periods. In this …
quantitatively measure biological processes rapidly and over long time periods. In this …
AMP-Net: Denoising-based deep unfolding for compressive image sensing
Most compressive sensing (CS) reconstruction methods can be divided into two categories,
ie model-based methods and classical deep network methods. By unfolding the iterative …
ie model-based methods and classical deep network methods. By unfolding the iterative …