Deep Learning in Medical Imaging: General Overview.
10.3348/kjr.2017.18.4.570
- Author:
June Goo LEE
1
;
Sanghoon JUN
;
Young Won CHO
;
Hyunna LEE
;
Guk Bae KIM
;
Joon Beom SEO
;
Namkug KIM
Author Information
1. Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea.
- Publication Type:Review
- Keywords:
Artificial intelligence;
Machine learning;
Convolutional neural network;
Recurrent Neural Network;
Computer-aided;
Precision medicine;
Radiology
- MeSH:
Artificial Intelligence;
Computer Systems;
Delivery of Health Care;
Diagnostic Imaging*;
Humans;
Machine Learning*;
Neurons;
Precision Medicine;
Synapses
- From:Korean Journal of Radiology
2017;18(4):570-584
- CountryRepublic of Korea
- Language:English
-
Abstract:
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 limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.
