Lee June-Goo, Jun Sanghoon, Cho Young-Won, Lee Hyunna, Kim Guk Bae, Seo Joon Beom, Kim Namkug
Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea.
Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea.
Korean J Radiol. 2017 Jul-Aug;18(4):570-584. doi: 10.3348/kjr.2017.18.4.570. Epub 2017 May 19.
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.
人工神经网络（ANN）是一种受人类神经元突触系统启发的机器学习技术，于20世纪50年代被引入。然而，由于深度架构训练中存在梯度消失和过拟合问题、缺乏计算能力，以及主要是缺乏足够的数据来训练计算机系统，ANN以前在解决实际问题的能力方面受到限制。由于大数据的可用性、当前图形处理单元增强的计算能力以及用于训练深度神经网络的新颖算法，人们对这一概念的兴趣最近重新浮现。最近对这项技术的研究表明，它在某些视觉和听觉识别任务中有可能比人类表现得更好，这可能预示着它在可预见的未来在医学和医疗保健领域，特别是在医学成像中的应用。这篇综述文章提供了关于深度学习技术的历史、发展和应用的观点，特别是关于其在医学成像中的应用。
