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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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由于放射科医生对计算机化病变检测培训数据的注释差异,性能变化.

Yukihiro Nomura1,2, Shouhei Hanaoka3,4, Naoto Hayashi5

  • 1Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, 263-8522, Japan. ynomura@chiba-u.jp.

International journal of computer assisted radiology and surgery
|April 16, 2024
PubMed
概括
此摘要是机器生成的。

放射科医生经验与影响计算机辅助检测 (CAD) 软件性能的注释变异性没有相关性. 用集成注释重新训练CAD显示了根据软件的不同结果,影响了诊断准确性.

关键词:
标注注释 标注注释计算机辅助检测 (CAD) 是一种计算机辅助检测.机器学习是机器学习.重新培训是什么意思

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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
  • 放射学信息学 放射学信息学

背景情况:

  • 放射科医生的注释质量显著影响基于机器学习的计算机辅助检测 (CAD) 软件性能.
  • 放射科医生在图像解释方面的经验是注释变化的潜在来源.

研究的目的:

  • 调查不同放射科医生经验如何影响注释变化.
  • 评估重新培训CAD软件对其性能的影响,包括不同经验水平的放射科医生的注释.
  • 评估多位放射科医生的综合注释对CAD软件性能的影响.

主要方法:

  • 利用两种CAD软件类型来检测肺结节和脑动脉瘤.
  • 十二位具有不同经验水平的放射科医生独立注释了病变.
  • 通过重复的CAD软件再培训,以个人和综合的放射科医生注释来研究性能变化.

主要成果:

  • 在重新培训后,CAD软件的性能与不同放射科医生的注释有很大差异.
  • 在某些情况下,再培训导致软件性能与初始版本相比降低.
  • 综合注释显示了基于CAD软件类型的各种性能趋势,与单一注释器使用相比,脑动脉瘤检测的性能下降.

结论:

  • 放射科医生经验和影响CAD表现的注释变异性之间没有直接相关性.
  • 综合注释的再培训对绩效的影响因具体的CAD软件而异.
  • 放射科医生之间的注释变化可能会影响CAD软件的性能,需要在再培训和开发过程中仔细考虑.