Departments of Medicine and Radiology, McMaster University, Hamilton, Ontario, Canada
Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada.
J Nucl Med. 2021 Jan;62(1):22-29. doi: 10.2967/jnumed.119.231837. Epub 2020 Sep 25.
This article is the second part in our machine learning series. Part 1 provided a general overview of machine learning in nuclear medicine. Part 2 focuses on neural networks. We start with an example illustrating how neural networks work and a discussion of potential applications. Recognizing that there is a spectrum of applications, we focus on recent publications in the areas of image reconstruction, low-dose PET, disease detection, and models used for diagnosis and outcome prediction. Finally, since the way machine learning algorithms are reported in the literature is extremely variable, we conclude with a call to arms regarding the need for standardized reporting of design and outcome metrics and we propose a basic checklist our community might follow going forward.
这是我们机器学习系列的第二部分。第一部分提供了核医学中机器学习的概述。第二部分重点介绍神经网络。我们从一个示例开始，说明神经网络的工作原理以及潜在应用的讨论。认识到应用范围广泛，我们专注于图像重建、低剂量 PET、疾病检测以及用于诊断和预后预测的模型等领域的最新出版物。最后，由于机器学习算法在文献中的报告方式极其多样化，我们呼吁需要标准化报告设计和结果指标，并提出我们的社区在未来可能遵循的基本清单。
