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Updated: Jul 5, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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双一致性规范化与主观逻辑用于半监督医疗图像细分半监督医疗图像细分.

Shanfu Lu1, Ziye Yan1, Wei Chen2

  • 1Perception Vision Medical Technologies Co., Ltd, Guangzhou, 510530, China.

Computers in biology and medicine
|January 19, 2024
PubMed
概括

本研究引入了双一致性规范化与主观逻辑,通过更好地利用未标记的数据和估计不确定性来改善半监督的医疗图像细分.

关键词:
双一致性规范化的规范化医疗图像细分 医疗图像细分半监督学习 半监督学习主观逻辑的主观逻辑不确定性估计估计不确定性

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科学领域:

  • 计算机视觉 计算机视觉
  • 医学图像分析 医学图像分析
  • 机器学习 机器学习

背景情况:

  • 半监督学习对于降低医学成像中的数据标签成本至关重要.
  • 现有的方法,如一致性规范化和伪标签,由于对未标签数据的认识不足,可能会被误导.

研究的目的:

  • 提出一种新的双一致性规范化方法,使用主观逻辑进行半监督医疗图像细分.
  • 通过解决当前处理未标记数据的方法的局限性来增强模型指导.

主要方法:

  • 引入了主观逻辑来估计半监督医疗图像细分中的不确定性.
  • 在基于一致性假设的弱和强扰动下开发了双一致性规范化.
  • 在ACDC,LA和胰腺数据集上评估了该方法.

主要成果:

  • 与现有最先进的 (SOTA) 技术相比,提出的方法显示出更好的性能.
  • 通过估计不确定性,有效指导模型的学习过程使用未标记的数据.

结论:

  • 与主观逻辑的双一致性规范化为半监督医疗图像细分提供了一个有希望的方法.
  • 该方法提高了未标记数据的利用率,并提高了细分精度.