说明：  更好地解释图像的计算机辅助分析一直是医学成像领域的长期问题。在图像理解方面，机器学习的最新进展，特别是在深度学习方面，在帮助识别、分类和量化医学图像模式方面有了很大的飞跃。在这篇文章中，介绍了深层学习方法的基础；回顾它们在图像配准、解剖/细胞结构检测、组织分割、计算机辅助疾病诊断或预后等方面的成功。最后，提出了研究问题，并提出了进一步改进的未来方向。
(The computer-assisted analysis for better interpreting images have been longstanding issues in the medical imaging field. On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images. In this article, we introduce the fundamentals of deep learning methods; review their successes to image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease diagnosis or prognosis, and so on. We conclude by raising research issues and suggesting future directions for further improvements.)