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基于本地区域的半监督肝脏细分自主监督.

Qiong Lou1, Tingyi Lin1, Yaguan Qian1

  • 1School of Science, Zhejiang University of Science and Technology, Hangzhou, China.

Medical physics
|December 18, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种使用对比学习和局部区域自我监督 (LRS2) 的新型半监督肝脏细分方法. 这种方法有效地利用了不可靠的预测,与监督方法相比,将子系数提高了6.11%.

关键词:
相反的学习学习学习.肝脏细分 细分肝脏的细分形态操作 形态操作半监督学习 半监督学习

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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
  • 计算机视觉 计算机视觉

背景情况:

  • 半监督学习减少了医学图像细分中的注释需求.
  • 相反的学习辅助工具在利用不可靠的预测,但可以忽略解剖结构.
  • 整合解剖学上下文对于优化医疗图像细分至关重要.

研究的目的:

  • 提出一种新的半监督方法,使用对比学习来对肝脏进行细分.
  • 为了提高对比学习对医疗图像细分任务的有效性.
  • 为了利用未标记的数据来提高肝脏细分的准确性.

主要方法:

  • 提出了一种半监督的对比学习方法,其中地方区域进行自我监督 (LRS2).
  • 利用香农来区分可靠和不可靠的预测,减少区域单位内的表示差异.
  • 引入了动态可靠性值,并应用了形态操作 (侵蚀,扩张) 来精细选择样本.

主要成果:

  • 通过有效利用不可靠的预测,LRS2方法表现出令人满意的性能.
  • 与监管的VNet相比,在Dice系数方面取得了显著的改进,收益高达6.11%.
  • 报告的子系数在不同的标记数据比例中从93.31%到95.12%不等.

结论:

  • 拟议的方法成功地从可靠区域选择了正和负样本,在不可靠区域正确地分配了像素.
  • 通过区域分区将解剖结构纳入,可以提高样本信息捕获的精度.
  • 广泛的实验验证了LRS2方法对半监督肝脏细分的有效性.