Mitigating Bias in Radiology Machine Learning: 3. Performance Metrics
Mitigating Bias in Radiology Machine Learning: 3. Performance Metrics
复制标题
DOI：
10.1148/ryai.220061
复制
发表时间：
2022-09-01
期刊：
影响因子：
--
通讯作者：
Erickson, Bradley J.
中科院分区：
文献类型：
--
作者：
Faghani, Shahriar;Khosravi, Bardia;Erickson, Bradley J.
机器学习（ML）算法在临床环境中的使用越来越多，引起了人们对ML模型偏差的担忧。偏见可能出现在ML创建的任何步骤，包括数据处理、模型开发和性能评估。ML模型中的潜在偏差可以通过正确实施这些步骤来最小化。本报告侧重于性能评估，并讨论了模型适应性，以及一套性能评估工具箱：即性能指标，性能解释图和不确定性量化。通过讨论每个工具箱的优势和局限性，我们的报告强调了在放射学人工智能模型的性能评估期间减轻和检测偏差的策略和考虑因素。
The increasing use of machine learning (ML) algorithms in clinical settings raises concerns about bias in ML models. Bias can arise at any step of ML creation, including data handling, model development, and performance evaluation. Potential biases in the ML model can be minimized by implementing these steps correctly. This report focuses on performance evaluation and discusses model fitness, as well as a set of perfor-mance evaluation toolboxes: namely, performance metrics, performance interpretation maps, and uncertainty quantification. By discussing the strengths and limitations of each toolbox, our report highlights strategies and considerations to mitigate and detect biases during performance evaluations of radiology artificial intelligence models.