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从不完整的注释中进行强大的深度学习,以准确检测肺结节.

Zebin Gao1, Yuchen Guo2, Guoxin Wang3

  • 1School of Information Science and Technology, Fudan University, Shanghai 200438, China.

Computers in biology and medicine
|April 3, 2024
PubMed
概括

本研究介绍了FULFIL,该算法在低剂量计算机断层扫描 (LDCT) 中使用不完整的注释来检测肺结节. 它以显著降低的注释成本实现了专家级别的性能,有助于肺癌诊断.

关键词:
深度学习是一种深度学习.图形卷积网络中的图形卷积网络.肺结节检测 肺结节检测缺乏监督的学习学习.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算机科学 计算机科学

背景情况:

  • 在LDCT扫描中检测肺结节的深度学习对于肺癌诊断至关重要.
  • 目前的方法需要广泛的,精心注释的数据集,增加成本和时间.
  • 不完整的注释对培养有效的深度学习模型构成挑战.

研究的目的:

  • 开发一种创新的算法,FULFIL,用于使用不完全注释的数据集检测肺结节.
  • 通过让注释员只标记自信的节点来降低注释成本.
  • 为了使强大的深度学习模型能够进行自我适应式学习和注释完成.

主要方法:

  • 利用图形卷积网络 (GCN) 通过发现注释和未注释节点之间的关系来完成自适应注释.
  • 在完成的注释数据集上使用教师-学生框架进行自我适应的学习.
  • 设计了一个双视图损失函数,以增强功能稳定性和模型概括性.

主要成果:

  • 通过使用Luna数据集中仅10%的实例级注释,达到0.074的灵敏度在0.125每扫描假阳性 (FPs/scan).
  • 它的表现比比较方法高出7.00%.
  • 在实验性比较中证明了与人类专家可比的性能.

结论:

  • FULFIL有效地利用不完整的肺结节数据集来开发强大的深度学习模型.
  • 该算法显著降低了注释成本,同时保持了高检测性能.
  • 富菲尔是一个有前途的工具,可以帮助检测肺结节和诊断肺癌.