Deep Learning: A Primer for Radiologists
Deep Learning: A Primer for Radiologists
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DOI：
10.1148/rg.2017170077
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发表时间：
2017-11-01
期刊：
影响因子：
5.5
通讯作者：
Tang, An
中科院分区：
文献类型：
--
作者：
Chartrand, Gabriel;Cheng, Phillip M.;Tang, An
深度学习是一类机器学习方法，在许多领域取得了成功并引起了人们的兴趣，包括计算机视觉，语音识别，自然语言处理和玩游戏。深度学习方法产生从原始输入到期望输出（例如，图像类）的映射。与传统的机器学习方法不同，它需要从输入中手工提取特征，深度学习方法直接从数据中学习这些特征。随着大型数据集的出现和计算能力的提高，这些方法可以产生具有出色性能的模型。这些模型是多层人工神经网络，松散地受到生物神经系统的启发。基于输入和目标输出的示例对，通过将校正误差信号反向传播通过网络来迭代地调整网络中的节点（神经元）之间的加权连接。对于计算机视觉任务，卷积神经网络（CNN）已被证明是有效的。最近，已经提出了CNN的几种临床应用，并在放射学中研究了分类，检测和分割任务。本文回顾了临床放射科医生深度学习的关键概念，讨论了技术要求，描述了临床放射学中的新兴应用，并概述了该领域的局限性和未来发展方向。放射科医生应该熟悉深度学习在医学成像中的原理和潜在应用。(C)RSNA，2017
Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. (C) RSNA, 2017