Machine Learning for Medical Imaging.
Machine Learning for Medical Imaging.
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DOI：
10.1148/rg.2017160130
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发表时间：
2017-03
期刊：
影响因子：
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通讯作者：
Kline TL
中科院分区：
文献类型：
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作者：
Erickson BJ;Korfiatis P;Akkus Z;Kline TL
机器学习是一种用于识别可应用于医学图像的模式的技术。虽然它是一个强大的工具，可以帮助提供医疗诊断，它可以被误用。机器学习通常开始于机器学习算法系统计算被认为在进行感兴趣的预测或诊断中具有重要性的图像特征。然后，机器学习算法系统识别这些图像特征的最佳组合，用于对图像进行分类或计算给定图像区域的某个度量。有几种方法可以使用，每种方法都有不同的优点和缺点。这些机器学习方法中的大多数都有开源版本，使它们易于尝试并应用于图像。存在几个度量算法性能的指标;然而，必须意识到可能导致误导性指标的相关陷阱。最近，深度学习已经开始使用;这种方法的好处是它不需要将图像特征识别和计算作为第一步;相反，将特征识别作为学习过程的一部分。机器学习已经应用于医学成像领域，未来将产生更大的影响。从事医学成像工作的人必须了解机器学习是如何工作的。
Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works.