医学成像分类中的自我监督和监督学习:解决工作流设计的隐藏偏见
概括
在医学成像中,自我监督学习 (SSL) 和监督学习 (SL) 之间的比较往往因工作流差异而偏见. 将两阶段的工作流应用于SL可以改变性能,影响AI驱动的医疗保健解决方案.
科学领域:
- 医学成像人工智能 医学成像人工智能
- 医疗保健中的机器学习
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 自主监督学习 (SSL) 通过利用未标记的数据显示出医学成像的前景.
- 由于培训工作流程不同,SSL和监督学习 (SL) 之间的传统比较可能存在缺陷.
- 工作流差异可以引入偏见,影响学习范式的感知性能.
研究的目的:
- 调查培训工作流对SSL和SL在医学成像中的性能比较的影响.
- 为了确定工作流变化,而不是学习范式本身,混研究结果.
- 为医疗保健中更可靠的人工智能方法开发提供见解.
主要方法:
- 在四个不同的医学成像数据集 (大脑MRI,胸部RX,OCT) 上进行实验.
- 实施不同的标签可用性和类频率配置.
- 针对每个数据集配置的多个实验结果进行了强大的统计分析.
主要成果:
- 通常用于SSL评估的两阶段工作流,当应用于SL时,会显著改变模型性能.
- 工作流程的差异显然会在不同的医学成像任务中影响受过训练的模型性能.
- 统计分析证实,工作流变化,而不仅仅是学习范式,影响结果.
结论:
- 医疗成像中SSL和SL之间的公平比较需要工作流的标准化.
- 忽视的工作流偏见可能会导致关于学习范式有效性的不准确结论.
- 解决工作流不可知论对于在临床决策中推进可信的人工智能至关重要.
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