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 with noisy labels: Exploring techniques and remedies in medical image analysis
Supervised training of deep learning models requires large labeled datasets. There is a
growing interest in obtaining such datasets for medical image analysis applications …
growing interest in obtaining such datasets for medical image analysis applications …
[HTML][HTML] Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance
L Vandewinckele, M Claessens, A Dinkla… - Radiotherapy and …, 2020 - Elsevier
Artificial Intelligence (AI) is currently being introduced into different domains, including
medicine. Specifically in radiation oncology, machine learning models allow automation and …
medicine. Specifically in radiation oncology, machine learning models allow automation and …
[HTML][HTML] A gentle introduction to deep learning in medical image processing
This paper tries to give a gentle introduction to deep learning in medical image processing,
proceeding from theoretical foundations to applications. We first discuss general reasons for …
proceeding from theoretical foundations to applications. We first discuss general reasons for …
State of the art in total body PET
The idea of a very sensitive positron emission tomography (PET) system covering a large
portion of the body of a patient already dates back to the early 1990s. In the period 2000 …
portion of the body of a patient already dates back to the early 1990s. In the period 2000 …
CT super-resolution GAN constrained by the identical, residual, and cycle learning ensemble (GAN-CIRCLE)
In this paper, we present a semi-supervised deep learning approach to accurately recover
high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the …
high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the …
FISTA-Net: Learning a fast iterative shrinkage thresholding network for inverse problems in imaging
Inverse problems are essential to imaging applications. In this letter, we propose a model-
based deep learning network, named FISTA-Net, by combining the merits of interpretability …
based deep learning network, named FISTA-Net, by combining the merits of interpretability …
Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction
Commercial iterative reconstruction techniques help to reduce the radiation dose of
computed tomography (CT), but altered image appearance and artefacts can limit their …
computed tomography (CT), but altered image appearance and artefacts can limit their …
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 …
DU-GAN: Generative adversarial networks with dual-domain U-Net-based discriminators for low-dose CT denoising
Low-dose computed tomography (LDCT) has drawn major attention in the medical imaging
field due to the potential health risks of CT-associated X-ray radiation to patients. Reducing …
field due to the potential health risks of CT-associated X-ray radiation to patients. Reducing …