医学成像中人工智能的交叉验证指南
A Guide to Cross-Validation for Artificial Intelligence in Medical Imaging.
作者信息
Bradshaw Tyler J, Huemann Zachary, Hu Junjie, Rahmim Arman
机构信息
From the Departments of Radiology (T.J.B., Z.H.) and Biostatistics and Computer Science (J.H.), University of Wisconsin-Madison, 1111 Highland Ave, Madison, WI 53705; Departments of Radiology and Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada (A.R.); and Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, British Columbia, Canada (A.R).
出版信息
Radiol Artif Intell. 2023 May 24;5(4):e220232. doi: 10.1148/ryai.220232. eCollection 2023 Jul.
Artificial intelligence (AI) is being increasingly used to automate and improve technologies within the field of medical imaging. A critical step in the development of an AI algorithm is estimating its prediction error through cross-validation (CV). The use of CV can help prevent overoptimism in AI algorithms and can mitigate certain biases associated with hyperparameter tuning and algorithm selection. This article introduces the principles of CV and provides a practical guide on the use of CV for AI algorithm development in medical imaging. Different CV techniques are described, as well as their advantages and disadvantages under different scenarios. Common pitfalls in prediction error estimation and guidance on how to avoid them are also discussed. Education, Research Design, Technical Aspects, Statistics, Supervised Learning, Convolutional Neural Network (CNN) . © RSNA, 2023.
人工智能（AI）正越来越多地用于实现医学成像领域的技术自动化并对其进行改进。开发人工智能算法的关键一步是通过交叉验证（CV）来估计其预测误差。使用交叉验证有助于防止人工智能算法出现过度乐观的情况，并可以减轻与超参数调整和算法选择相关的某些偏差。本文介绍了交叉验证的原理，并提供了在医学成像中使用交叉验证进行人工智能算法开发的实用指南。文中描述了不同的交叉验证技术，以及它们在不同场景下的优缺点。还讨论了预测误差估计中的常见陷阱以及如何避免这些陷阱的指导意见。教育、研究设计、技术方面、统计学、监督学习、卷积神经网络（CNN）。©RSNA，2023年。
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