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相关概念视频

X-ray Imaging01:24

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with X-rays, and by 1900, X-ray was widely...

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通过对抗性训练增强牙损伤的分类,对咬牙X射线图进行训练.

Wattanapong Suttapak1, Wannakamon Panyarak2, Arnon Charuakkra2

  • 1Division of Computer Engineering, School of Information and Communication Technology, University of Phayao, Phayao, Thailand.

Journal of imaging informatics in medicine
|November 10, 2025
PubMed
概括

这项研究通过使用深度学习模型来提高牙损伤的分类,这些模型经过预测梯度下降 (PGD) 的训练. 使用PGD进行对抗训练可以提高AI的准确性和稳定性,用于从放射图片中诊断腔.

关键词:
产生对抗性攻击的攻击.脏病诊断 脏病诊断 脏病诊断牙虫的分类 牙虫的分类预计的梯度下降 (PGD)这就是ResNet ResNet.

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科学领域:

  • 牙科 牙科是指牙科的专业.
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 精确的牙损分类从X光片是诊断和治疗的重要.
  • 传统方法可能是主观的;计算机辅助诊断系统提供潜在的好处.
  • 深度学习模型显示使用ICCMSTM 7类系统对虫的分类具有前景.

研究的目的:

  • 通过将预测梯度下降 (PGD) 纳入培训过程来增强ResNet模型的牙腐烂分类.
  • 通过对抗数据增强,提高深度学习模型的稳定性和分类性能.

主要方法:

  • 使用预测梯度下降 (PGD) 增加一个清洁的数据集与轻微的干扰.
  • 培训ResNet模型,特别是ResNet-50,具有PGD增强数据集.
  • 使用验证和测试准确度,灵敏度和特异性指标评估模型性能.

主要成果:

  • 增强PGD的ResNet-50模型显示了显著的性能改进.
  • 验证准确度从58.20%增加到67.20%.
  • 测试准确性从57.14%提高到59.18%,敏感性和特异性显著提高.

结论:

  • 像PGD这样的对抗性培训技术可以大大提高牙虫分类的深度学习模型的准确性,稳定性和可靠性.
  • 这些发现支持用于临床牙科实践的计算机辅助诊断工具的进步.
  • 增强PGD提供了一种有前途的方法来增强人工智能驱动的牙科诊断.