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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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提高结直肠癌注释效率的方法,用于人工智能观察员培训.

Matthew Grudza1, Brandon Salinel2, Sarah Zeien3

  • 1School of Biological Health and Systems Engineering, Arizona State University, Tempe, AZ 85287, United States.

World journal of radiology
|January 5, 2024
PubMed
概括

稀少的注释显著减少了结肠直肠癌 (CRC) 检测AI模型培训时间,而不会影响准确性. 这种方法有效地建立了人工智能开发的基本真相.

关键词:
人工智能的人工智能是人工智能.结肠直肠癌是一种癌症.检测 检测 检测 检测 检测

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科学领域:

  • 放射学 放射学是指放射学
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 缺失隐性癌症病变是放射学诊断错误的主要原因之一.
  • 作为第二个观察者的人工智能 (AI) 为减少这些错误提供了一个经济的解决方案.
  • 大量的注释数据集对于在癌症检测方面有效的AI模型培训至关重要.

研究的目的:

  • 为了比较跳过切片注释和人工智能启动的注释,以减少人工智能模型训练时间.
  • 评估不同注释方法在建立人工智能开发的基本真相方面的效率.

主要方法:

  • 开发了一个用于结直肠癌 (CRC) 检测的2D U-Net AI模型.
  • 采用了一组2D U-Nets,以提高性能.
  • 使用癌症成像档案数据集训练和测试模型,比较跳过切片和人工智能启动的注释技术.

主要成果:

  • 稀疏的注释,特别是跳过两个切片,显著减少了注释时间 (P < 0.001).
  • 减少注释 (高达2/3) 并没有对AI模型灵敏度或假阳性率产生负面影响.
  • 人工智能启动的注释提供了最小的时间缩短,即使使用集体人工智能方法.

结论:

  • 稀疏注释是一种有效的技术,可以减少为AI模型建立基本真相所需的时间.
  • 这种方法支持人工智能工具的更快发展,以改进癌症病变检测.