通过人工智能协作标签,加快横截图的语音注释,以质量保证和偏差缓解为准
David Dreizin1, Lei Zhang1, Nathan Sarkar1
1Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, United States.
Frontiers in radiology
|July 24, 2023
概括
人工智能协作标签 (AICL) 显著减少了注释时间,并提高了医学成像标签的标签质量. 这种方法可以有效地创建高质量的地面真相数据,用于人工智能培训.
科学领域:
- 医学成像医学成像
- 医学中的人工智能
- 定量工具是指量化工具.
背景情况:
- 精准医学需要对医疗图像进行准确的标签,这是耗时且难以扩展的.
- 人工智能生成的标签可以手动编辑 (人工智能协作标签,AICL) 以节省时间,但其有效性尚未得到充分记录.
- 编辑的AI标签存在自动化偏差的潜力.
研究的目的:
- 为了评估AICL的时间节约和标签质量,在试点研究中使用CT扫描内腔内出血.
- 提出使用AICL注释作为高通量,高质量的地面真相数据的标准.
主要方法:
- 分析了57个带有创伤性腔内出血的CT扫描.
- nnU-net模型用于初始基于AI的标签.
- AICL与de novo专家手册标签和专家编辑人员标签进行了比较.
- 评估了时间费用和图像质量 (9分级);观察员试图区分AICL和人类标签.
主要成果:
- AICL的注释时间比员工标签编辑减少了2.8倍,比专家标签减少了8.7倍 (p < 0.0006).
- AICL标签获得的质量等级 (8.4) 比专家 (7.8) 和编辑人员标签 (7.7) 高得多 (p < 0.0006).
- 一个独立的观察员无法可靠地区分AICL和人类生成的标签.
结论:
- 通过AICL,可以快速大规模整理高质量的地面真实数据,用于医疗图像细分任务.
- 研究人员可以通过提出的质量控制措施来实施AICL.
- 这种方法支持用于临床应用的强大和可通用的AI模型的开发.
关键词:
人工智能辅助注释人工智能协作标签的标签CT体积测量 CT体积测量人工智能AI 的人工智能AI计算机断层扫描 (CT) 是一种计算机断层扫描.循环中的人类 (HITL)定量化可视化 定量化可视化创伤 创伤 创伤 创伤 创伤更多相关视频
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