人类不在循环中:课程学习的客观样本难度指标
Zhengbo Zhou1, Jun Luo1, Dooman Arefan2
1Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
Proceedings. IEEE International Symposium on Biomedical Imaging
|October 27, 2023
概括
本研究介绍了一种使用渐变差 (VoG) 的自动化方法,用于医学图像分析中的课程学习. 这种方法客观地测量样本的难度,改善骨折分类性能,没有人类偏见.
科学领域:
- 医学成像分析分析 医学成像分析
- 机器学习在医疗保健中的应用
- 放射学中的人工智能
背景情况:
- 课程学习使用样本难度顺序训练模型,这对于有效的机器学习至关重要.
- 医疗图像分类的现有方法依赖于主观的人类专业知识,引入偏差和额外的注释成本.
- 客观和自动化的难度测量是医疗领域强大的课程学习所需的.
研究的目的:
- 提出和评估一种用于医学图像分类的新型自动化课程学习技术.
- 引入梯度的方差 (VoG) 作为样本难度的客观度量.
- 评估VoG指导课程学习对X射线图像的肘部骨折分类的有效性.
主要方法:
- 开发了一种自动化的课程学习方法,利用梯度变量 (VoG) 来量化样本分类难度.
- 根据VoG分数对医疗图像样本进行排名,较高的分数表明分类难度更高.
- 雇员 VoG 指导的课程学习用于训练X射线图像上的分类模型.
- 将 VoG 方法与基线 (没有课程学习),人类注释困难和反课程学习进行了比较.
主要成果:
- 拟议的基于VoG的课程学习在二元和多类骨折分类任务中实现了可比且优异的性能.
- 证明了自动化,客观难度测量的有效性,以提高模型培训.
- 超过或匹配传统方法,包括那些依赖于人类注释的方法.
结论:
- 使用梯度变异 (VoG) 的自动课程学习为医学图像分析中的人为指导方法提供了有效和客观的替代方案.
- VoG技术成功地提高了X射线图像中骨折的分类性能.
- 这种方法减少了对主观专业知识和注释努力的依赖,为医疗诊断中更有效的AI铺平了道路.
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