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相关实验视频

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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基于证据的不确定性意识的半监督医疗图像细分基于证据的不确定性意识.

Yingyu Chen1, Ziyuan Yang2, Chenyu Shen2

  • 1College of Computer Science, Sichuan University, China; The Key Laboratory of Data Protection and Intelligent Management, Ministry of Education, Sichuan University, China.

Computers in biology and medicine
|January 26, 2024
PubMed
概括

医学成像中的半监督学习 (SSL) 可以通过使用证据推断学习 (EVIL) 来改进. 通过量化不确定性,EVIL减少了伪标签中的错误,提高了细分的准确性.

关键词:
医疗图像细分 医疗图像细分半监督学习 半监督学习不确定性估计估计不确定性

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科学领域:

  • 医学图像分析 医学图像分析
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 半监督学习 (SSL) 减少了临床环境中的注释需求.
  • 在SSL中伪标签可以引入降低性能的错误.
  • 现有的不确定性意识方法面临成本,准确性和理论上的权衡.

研究的目的:

  • 在医学图像细分中引入证据推断学习 (EVIL) 用于不确定性意识的SSL.
  • 为了解决目前不确定性意识SSL方法的局限性.
  • 为了提高医疗图像细分的可靠性和准确性.

主要方法:

  • 在SSL中集成Dempster-Shafer证据理论 (DST).
  • 开发了EVIL作为一个一致性规范化培训范式.
  • 在单个前进传递中实现了精确的不确定性量化.
  • 根据不确定性估计,丢弃了不可靠的伪标签.

主要成果:

  • EVIL在与最先进的方法相比,表现出了竞争力的表现.
  • 这种方法在公共数据集上得到了验证:ACD,MM-WHS和MonuSeg.
  • 实现了可靠的伪标签生成和改进的细分.

结论:

  • 在SSL中,EVIL为不确定性量化提供了理论上可靠且计算效率高的解决方案.
  • 该方法通过利用可信的伪标签有效地改善了医疗图像细分.
  • 在临床应用中,EVIL为强大的SSL提供了一个有希望的方向.