Deep Learning in Medical Imaging: General Overview. Lee, J., Jun, S., Cho, Y., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. Korean journal of radiology, 18(4):570–584, August, 2017. Place: Korea (South)doi abstract bibtex 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.
@article{lee_deep_2017,
title = {Deep {Learning} in {Medical} {Imaging}: {General} {Overview}.},
volume = {18},
issn = {2005-8330 1229-6929},
doi = {10.3348/kjr.2017.18.4.570},
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.},
language = {eng},
number = {4},
journal = {Korean journal of radiology},
author = {Lee, June-Goo and Jun, Sanghoon and Cho, Young-Won and Lee, Hyunna and Kim, Guk Bae and Seo, Joon Beom and Kim, Namkug},
month = aug,
year = {2017},
pmid = {28670152},
pmcid = {PMC5447633},
note = {Place: Korea (South)},
keywords = {*Algorithms, *Neural Networks, Computer, Artificial intelligence, Computer-aided, Convolutional neural network, Humans, Image Processing, Computer-Assisted, Knee/diagnostic imaging, Machine learning, Magnetic Resonance Imaging, Optical Imaging/methods, Precision medicine, Radiology, Recurrent Neural Network},
pages = {570--584},
}
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