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 …
Convolutional neural networks: an overview and application in radiology
R Yamashita, M Nishio, RKG Do, K Togashi - Insights into imaging, 2018 - Springer
Convolutional neural network (CNN), a class of artificial neural networks that has become
dominant in various computer vision tasks, is attracting interest across a variety of domains …
dominant in various computer vision tasks, is attracting interest across a variety of domains …
Iterative reconstruction of low-dose CT based on differential sparse
The commonly used method to reduce the dose is to reduce the tube current. The number of
photons received by the detector decreases, making the CT image obtained by analytical …
photons received by the detector decreases, making the CT image obtained by analytical …
An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning
Deep learning for digital pathology is hindered by the extremely high spatial resolution of
whole-slide images (WSIs). Most studies have employed patch-based methods, which often …
whole-slide images (WSIs). Most studies have employed patch-based methods, which often …
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 …
Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine
F Pesapane, M Codari, F Sardanelli - European radiology experimental, 2018 - Springer
One of the most promising areas of health innovation is the application of artificial
intelligence (AI), primarily in medical imaging. This article provides basic definitions of terms …
intelligence (AI), primarily in medical imaging. This article provides basic definitions of terms …
The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence
MJ Willemink, PB Noël - European radiology, 2019 - Springer
The first CT scanners in the early 1970s already used iterative reconstruction algorithms;
however, lack of computational power prevented their clinical use. In fact, it took until 2009 …
however, lack of computational power prevented their clinical use. In fact, it took until 2009 …
Convolutional neural networks for radiologic images: a radiologist's guide
S Soffer, A Ben-Cohen, O Shimon, MM Amitai… - Radiology, 2019 - pubs.rsna.org
Deep learning has rapidly advanced in various fields within the past few years and has
recently gained particular attention in the radiology community. This article provides an …
recently gained particular attention in the radiology community. This article provides an …
Low-dose CT with a residual encoder-decoder convolutional neural network
Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a
considerable interest in the medical imaging field. Currently, the main stream low-dose CT …
considerable interest in the medical imaging field. Currently, the main stream low-dose CT …
Deep convolutional neural network for inverse problems in imaging
In this paper, we propose a novel deep convolutional neural network (CNN)-based
algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have …
algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have …