From the Department of Radiology, Keck School of Medicine of the University of Southern California, Los Angeles, Calif (P.M.C.); Research Center (E.M., F.P.R., S.K., A.T.) and Department of Radiology (A.T.), Centre Hospitalier de l'Université de Montréal, 1058-2117 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (R.Y.); Warren Alpert Medical School, Brown University, Providence, RI (I.P.); Department of Medical Imaging, CISSS Lanaudière, Université Laval, Joliette, Québec, Canada (A.C.C., S.K.); École Polytechnique, Montréal, Québec, Canada (F.P.R.); and AFX Medical, Montréal, Québec, Canada (G.C.).
Radiographics. 2021 Sep-Oct;41(5):1427-1445. doi: 10.1148/rg.2021200210.
Deep learning is a class of machine learning methods that has been successful in computer vision. Unlike traditional machine learning methods that require hand-engineered feature extraction from input images, deep learning methods learn the image features by which to classify data. Convolutional neural networks (CNNs), the core of deep learning methods for imaging, are multilayered artificial neural networks with weighted connections between neurons that are iteratively adjusted through repeated exposure to training data. These networks have numerous applications in radiology, particularly in image classification, object detection, semantic segmentation, and instance segmentation. The authors provide an update on a recent primer on deep learning for radiologists, and they review terminology, data requirements, and recent trends in the design of CNNs; illustrate building blocks and architectures adapted to computer vision tasks, including generative architectures; and discuss training and validation, performance metrics, visualization, and future directions. Familiarity with the key concepts described will help radiologists understand advances of deep learning in medical imaging and facilitate clinical adoption of these techniques. RSNA, 2021.
深度学习是一类机器学习方法，已在计算机视觉领域取得成功。与需要从输入图像中手动提取特征的传统机器学习方法不同，深度学习方法通过学习图像特征来对数据进行分类。卷积神经网络（CNN）是用于成像的深度学习方法的核心，是一种具有神经元间加权连接的多层人工神经网络，通过对训练数据的反复暴露，神经元的连接权重得以迭代调整。这些网络在放射学中有许多应用，特别是在图像分类、目标检测、语义分割和实例分割方面。作者对最近发表的一篇放射科医生深度学习入门指南进行了更新，回顾了术语、数据要求以及 CNN 设计的最新趋势；说明了适用于计算机视觉任务的构建块和架构，包括生成式架构；并讨论了训练和验证、性能指标、可视化以及未来方向。熟悉所描述的关键概念将有助于放射科医生了解深度学习在医学成像中的进展，并促进这些技术在临床中的应用。RSNA，2021 年。
