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基于深度神经网络的自动二心染色体检测,使用对常见物体进行预训练的模型.

Kangsan Kim1, Kwang Seok Kim2, Won Il Jang2

  • 1Division of Applied RI, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Republic of Korea.

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概括

这项研究使用YOLOv5深度学习自动化了二心染色体测定 (DCA),显著改善了辐射剂量估计. 人工智能模型有效地检测染色体图像中的辐射诱导的DNA变化.

关键词:
染色体元相图像 染色体元相图像细胞遗传学剂量计深度学习是一种深度学习.双中心染色体测定对象检测检测对象检测对象检测转移学习转移学习你只看一次,你只看一次.

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科学领域:

  • 细胞遗传学 细胞遗传学
  • 辐射保护 辐射保护
  • 人工智能的人工智能

背景情况:

  • 二心染色体测定 (DCA) 是估计辐射剂量的关键方法.
  • 目前的DCA方法是劳动密集型的,需要专门的技能.
  • 自动化DCA可以提高生物对称的效率和准确性.

研究的目的:

  • 开发和评估一种深度学习模型,用于自动检测元相图像中的二中心染色体.
  • 评估YOLOv5算法用于二心染色体识别的性能.
  • 以有限的数据来证明使用预训练模型进行高效训练的可行性.

主要方法:

  • 利用YOLOv5,一个单阶段物体检测算法,用于染色体元相图像的自动分析.
  • 在887张增强染色体图像上训练了YOLOv5模型,利用预训练的参数.
  • 使用验证 (380图像) 和测试 (300图像) 数据集评估模型性能.

主要成果:

  • 预训练的YOLOv5模型在检测二心染色体方面获得了0.94的最大F1得分和0.961的平均平均精度 (mAP).
  • 一个随机初始化的模型显示性能下降,最大F1得分为0.82%,mAP为0.873%.
  • 结果证实了该模型在准确检测二心染色体方面的有效性.

结论:

  • 使用YOLOv5的基于深度学习的对象检测可以有效地自动化二心染色体检测.
  • 预先训练的模型可以使用相对较小的辐射生物测量数据集进行高效的训练.
  • 自动化DCA有望实现更快,更容易获得的辐射剂量评估.