卷积神经网络
医学影像学
抽象
机器学习
计算机科学
领域(数学)
深层神经网络
深度学习
原始数据
光学(聚焦)
人工神经网络
人工智能
模式识别(心理学)
认识论
光学
物理
哲学
程序设计语言
纯数学
数学
作者
Hayit Greenspan,Bram van Ginneken,Ronald M. Summers
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2016-05-01
卷期号:35 (5): 1153-1159
被引量:1358
标识
DOI:10.1109/tmi.2016.2553401
摘要
The papers in this special section focus on the technology and applications supported by deep learning. Deep learning is a growing trend in general data analysis and has been termed one of the 10 breakthrough technologies of 2013. Deep learning is an improvement of artificial neural networks, consisting of more layers that permit higher levels of abstraction and improved predictions from data. To date, it is emerging as the leading machine-learning tool in the general imaging and computer vision domains. In particular, convolutional neural networks (CNNs) have proven to be powerful tools for a broad range of computer vision tasks. Deep CNNs automatically learn mid-level and high-level abstractions obtained from raw data (e.g., images). Recent results indicate that the generic descriptors extracted from CNNs are extremely effective in object recognition and localization in natural images. Medical image analysis groups across the world are quickly entering the field and applying CNNs and other deep learning methodologies to a wide variety of applications.
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