Deep Learning in Medical Image Analysis.
Deep Learning in Medical Image Analysis.
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DOI：
10.1146/annurev-bioeng-071516-044442
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发表时间：
2017-06-21
影响因子：
9.7
通讯作者：
Suk HI
中科院分区：
文献类型：
--
作者：
Shen D;Wu G;Suk HI
为了更好地解释图像，计算机辅助分析一直是医学成像领域的一个长期问题。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取得了巨大的飞跃。具体地说，利用仅从数据学习的分层特征表示，而不是主要基于特定领域知识设计的手工特征，是进步的核心。通过这种方式，深度学习迅速被证明是最先进的基础，在各种医疗应用中实现了更好的性能。在本文中，我们介绍了深度学习方法的基本原理，综述了它们在图像配准、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，我们提出了研究问题，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. Specifically, exploiting hierarchical feature representations learned solely from data, instead of handcrafted features mostly designed based on domain-specific knowledge, lies at the core of the advances. In that way, deep learning is rapidly proving to be the state-of-the-art foundation, achieving enhanced performances in various medical applications. 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.