Levine Adrian B, Schlosser Colin, Grewal Jasleen, Coope Robin, Jones Steve J M, Yip Stephen
Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
Canada's Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.
Trends Cancer. 2019 Mar;5(3):157-169. doi: 10.1016/j.trecan.2019.02.002. Epub 2019 Feb 28.
Deep learning refers to a set of computer models that have recently been used to make unprecedented progress in the way computers extract information from images. These algorithms have been applied to tasks in numerous medical specialties, most extensively radiology and pathology, and in some cases have attained performance comparable to human experts. Furthermore, it is possible that deep learning could be used to extract data from medical images that would not be apparent by human analysis and could be used to inform on molecular status, prognosis, or treatment sensitivity. In this review, we outline the current developments and state-of-the-art in applying deep learning for cancer diagnosis, and discuss the challenges in adapting the technology for widespread clinical deployment.
深度学习指的是一组计算机模型，这些模型最近在计算机从图像中提取信息的方式上取得了前所未有的进展。这些算法已被应用于众多医学专科的任务中，最广泛应用于放射学和病理学，并且在某些情况下已达到与人类专家相当的性能。此外，深度学习有可能用于从医学图像中提取人类分析无法发现的数据，并可用于了解分子状态、预后或治疗敏感性。在本综述中，我们概述了将深度学习应用于癌症诊断的当前进展和最新技术，并讨论了将该技术应用于广泛临床部署时所面临的挑战。
