DIDSR/OSEL/CDRH U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA.
Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, USA.
Med Phys. 2019 Jan;46(1):e1-e36. doi: 10.1002/mp.13264. Epub 2018 Nov 20.
The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.
本文综述了深度学习（DL）在医学影像和放射治疗中的应用，旨在：(a) 总结目前已取得的成果；(b) 确定共同和独特的挑战，以及研究人员为应对这些挑战所采取的策略；(c) 确定未来在应用和技术创新方面的一些有前途的方向。我们介绍了 DL 和卷积神经网络的一般原理，调查了 DL 在医学影像和放射治疗中的五个主要应用领域，确定了共同的主题，讨论了数据集扩展的方法，最后总结了经验教训、遗留的挑战和未来的方向。
