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基于两阶段对抗式学习的无监督域调整用于视网膜OCT细分的OCT细分.

Shengyong Diao1, Ziting Yin1, Xinjian Chen1,2

  • 1MIPAV Lab, the School of Electronics and Information Engineering, Soochow University, Suzhou, China.

Medical physics
|March 1, 2024
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概括

本研究介绍了一个两阶段的对抗网络 (TSANet) 来解决光学连贯断层扫描 (OCT) 分段的域移动问题. TSANet 在没有手动标签的新数据集上提高了模型性能.

关键词:
具有对抗性的学习.深度学习是一种深度学习.视网膜OCT图像细分 视网膜OCT图像细分无监督的域名适应

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科学领域:

  • 医疗成像医学成像
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 对光学连贯性断层扫描 (OCT) 分段的深度学习使定量分析成为可能.
  • 由于不同的采集设备/协议导致的域名转移会降低细分模型的性能.
  • 解决领域转移对于强大的海外国家和地区图像分析至关重要.

研究的目的:

  • 提出一个两阶段的对抗性学习网络 (TSANet) 进行无监督的跨领域的OCT细分.
  • 克服因OCT成像中的域移动引起的性能退化.
  • 在不需要手动重新标记的情况下,在不同的OCT数据集中实现准确的细分.

主要方法:

  • 在第一阶段,采用福里埃转换方法进行图像级风格调整.
  • 使用对抗式学习与分段器和区分器来实现域间一致性.
  • 在第二阶段实施伪标签和微调,以提高概括性.

主要成果:

  • 在三个测试组中,在交叉与结合 (IoU) 中实现了8.34%,55.82%和3.53%的显著改进.
  • 与最先进的域名适应方法相比,证明了卓越的性能.
  • 在状腺和视网膜分裂细分任务上经过验证的有效性.

结论:

  • 通过多层次的适应策略,TSANet有效地实现了跨领域的泛化.
  • 在调整深度学习模型以适应新的海外国家和地区数据时,减少了手动注释的需要.
  • 促进了跨不同数据集的OCT细分模型的更广泛应用.