D Ueda, A Shimazaki, Y Miki - Japanese journal of radiology, 2019 - Springer
Deep learning has been applied to clinical applications in not only radiology, but also all other areas of medicine. This review provides a technical and clinical overview of deep …
JG Lee, S Jun, YW Cho, H Lee… - Korean journal of …, 2017 - synapse.koreamed.org
The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously …
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 …
K Suzuki - Radiological physics and technology, 2017 - Springer
The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis …
MP McBee, OA Awan, AT Colucci, CW Ghobadi… - Academic radiology, 2018 - Elsevier
As radiology is inherently a data-driven specialty, it is especially conducive to utilizing data processing techniques. One such technique, deep learning (DL), has become a remarkably …
Deep learning (DL) is one of the branches of artificial intelligence that has seen exponential growth in recent years. The scientific community has focused its attention on DL due to its …
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep …
Artificial intelligence (AI) is the most revolutionizing development in the health care industry in the current decade, with diagnostic imaging having the greatest share in such …
Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural …