医学成像中的人工智能交叉验证指南
Tyler J Bradshaw1, Zachary Huemann1, Junjie Hu1
1From 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).
交叉验证 (CV) 对于开发可靠的人工智能 (AI) 在医学成像中至关重要. 这种技术可以防止人工智能算法的过度乐观和偏见,确保对像卷积神经网络 (CNN) 这样的监督学习模型的准确预测错误估计.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 人工智能 (AI) 越来越多地成为医学成像技术的组成部分.
- 准确的AI算法开发需要强大的预测错误估计.
- 交叉验证 (CV) 是缓解人工智能模型偏见和过度乐观的一个关键技术.
研究的目的:
- 在医学成像中引入人工智能交叉验证 (CV) 的原则.
- 为应用CV在AI算法开发中提供实用指南.
- 讨论各种CV技术,它们的优缺点以及常见的陷.
主要方法:
- 交叉验证 (CV) 原则的解释.
- 不同CV技术的描述 (例如,k-fold,离开一个).
- 关于实际应用和避免预测错误估计中的常见错误的指导.
主要成果:
- 简历对于防止人工智能算法过度乐观至关重要.
- CV有助于减轻超参数调整和算法选择中的偏差.
- 了解不同的CV方法可以根据场景选择合适的技术.
结论:
- 交叉验证 (CV) 是医疗成像中可靠的人工智能开发的基本工具.
- 正确应用CV可确保更准确的预测错误估计.
- 本指南帮助研究人员有效地使用CV进行监督学习和CNN开发.
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