Convolutional neural networks: an overview and application in radiology.
Convolutional neural networks: an overview and application in radiology.
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DOI：
10.1007/s13244-018-0639-9
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发表时间：
2018-08
影响因子：
4.7
通讯作者：
Togashi K
中科院分区：
文献类型：
--
作者：
Yamashita R;Nishio M;Do RKG;Togashi K
卷积神经网络（CNN）是一类在各种计算机视觉任务中占主导地位的人工神经网络，正在吸引包括放射学在内的各种领域的兴趣。CNN旨在通过使用多个构建块（如卷积层，池化层和全连接层）通过反向传播自动和自适应地学习特征的空间层次结构。这篇综述文章提供了CNN的基本概念及其在各种放射学任务中的应用的视角，并讨论了其在放射学领域的挑战和未来方向。将CNN应用于放射性任务的两个挑战，小数据集和过拟合，也将在本文中讨论，以及最小化它们的技术。熟悉CNN的概念和优势以及局限性对于利用其在诊断放射学中的潜力至关重要，其目标是增强放射科医生的表现并改善患者护理。 卷积神经网络是一类深度学习方法，已在各种计算机视觉任务中占据主导地位，并吸引了包括放射学在内的各种领域的兴趣。 卷积神经网络由多个构建块组成，如卷积层、池化层和全连接层，旨在通过反向传播算法自动自适应地学习特征的空间层次。 熟悉卷积神经网络的概念和优点以及局限性，对于利用其潜力提高放射科医生的表现并最终提高患者护理水平至关重要。
Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology. CNN is designed to automatically and adaptively learn spatial hierarchies of features through backpropagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers. This review article offers a perspective on the basic concepts of CNN and its application to various radiological tasks, and discusses its challenges and future directions in the field of radiology. Two challenges in applying CNN to radiological tasks, small dataset and overfitting, will also be covered in this article, as well as techniques to minimize them. Being familiar with the concepts and advantages, as well as limitations, of CNN is essential to leverage its potential in diagnostic radiology, with the goal of augmenting the performance of radiologists and improving patient care. • Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. • Convolutional neural network is composed of multiple building blocks, such as convolution layers, pooling layers, and fully connected layers, and is designed to automatically and adaptively learn spatial hierarchies of features through a backpropagation algorithm. • Familiarity with the concepts and advantages, as well as limitations, of convolutional neural network is essential to leverage its potential to improve radiologist performance and, eventually, patient care.
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