Yasaka Koichiro, Akai Hiroyuki, Kunimatsu Akira, Kiryu Shigeru, Abe Osamu
Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.
Department of Radiology, Graduate School of Medical Sciences, International University of Health and Welfare, 4-3 Kozunomori, Narita, Chiba, Japan.
Jpn J Radiol. 2018 Apr;36(4):257-272. doi: 10.1007/s11604-018-0726-3. Epub 2018 Mar 1.
Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.
最近,使用卷积神经网络(CNN)的深度学习因其在图像识别方面的高性能而备受关注。利用这种技术,图像本身可用于学习过程,无需在学习过程之前进行特征提取。重要特征能够自动学习。除了深度学习技术之外,得益于硬件和软件的发展,该技术在放射图像上的应用开始得到研究,用于预测临床有用信息,如病变的检测和评估等。本文沿着实际过程(收集数据、实现卷积神经网络以及训练和测试阶段)阐述了有关卷积神经网络深度学习的基本技术知识。还说明了该技术存在的陷阱以及如何应对这些陷阱。我们还描述了深度学习的一些高级主题、近期临床研究的结果以及深度学习技术临床应用的未来方向。