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MUE-CoT:用于左心房细分的多尺度不确定性意识的联合培训框架.

Dechen Hao1, Hualing Li1, Yonglai Zhang1

  • 1School of Software, North University of China, Taiyuan Shanxi, People's Republic of China.

Physics in medicine and biology
|August 11, 2023
PubMed
概括

这项研究引入了一个新的半监督学习框架,用于准确的左心房细分,显著改善了有限的标记医疗数据的结果. MUE-CoT模型提高了细分的准确性,解决了临床分析中的注释成本挑战.

关键词:
联合培训是指联合培训.进入的过程中,左心房图像细分 左心房图像细分不确定性是一种不确定性.

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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
  • 计算生物学 计算生物学

背景情况:

  • 准确的左心房细分对于分析心房动至关重要.
  • 监督学习方法由于注释成本高而面临局限性.
  • 半监督学习提供了一个有前途的方法,使用有限的标记数据和大量的未标记数据.

研究的目的:

  • 为左心房细分开发一个高效的半监督学习框架.
  • 为了应对医疗图像分析中有限的标记数据的挑战.
  • 为了提高细分的准确性,同时减少手动注释的努力.

主要方法:

  • 提出了一个协作培训框架:多尺度不确定感知 (MUE-CoT).
  • 利用金字塔特征网络从未标记的数据中学习.
  • 引入了新的损失约束,包括多样性损失和联合培训的多尺度不确定性.
  • 实现了一个依赖于信心的实证高斯函数来权衡伪监督损失.

主要成果:

  • 与公共和内部数据集的现有半监督方法相比,MUE-CoT框架显示出更高的性能.
  • 获得了高的子相似系数 (例如84.94%±4.31) 和雅卡德相似系数 (例如74.00%±6.20),仅有5%的标记数据.
  • 报告了有利的HD95值 (例如4.63毫米±2.13),表明了精确的细分.

结论:

  • MUE-CoT模型有效地克服了医疗成像中的数据稀缺性和高注释成本.
  • 拟议的方法显著提高了左心房细分的准确性.
  • 这一框架有可能在临床分析和疾病识别方面得到实际应用.