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MIDC:基于深度学习的医学图像数据集清理框架.

Sanli Yi1,2, Ziyan Chen1,2

  • 1School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China.

Heliyon
|October 24, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个新的框架,医学图像数据集清理 (MIDC),自动删除错误标记的数据从医学成像数据集. 这可以提高卷积神经网络 (CNN) 的诊断准确性,而不需要专家医生标签.

关键词:
分类的准确性分类的准确性数据的清理数据的清理.深度学习是一种深度学习.错误标记的数据错误标记的数据公共医疗数据集是公共的.

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科学领域:

  • 医学成像和人工智能 医学成像和人工智能
  • 计算病理学计算病理学
  • 医疗保健中的机器学习

背景情况:

  • 深度学习,特别是卷积神经网络 (CNN),对于医学图像分析至关重要.
  • 高质量的数据集对于训练准确的CNN诊断模型至关重要.
  • 医疗数据集中的错误标签数据显著降低了诊断模型的性能,非专业医生很难识别这些错误.

研究的目的:

  • 提出和验证一个新的框架,医学图像数据集清理 (MIDC),用于自动识别和删除公共医学成像数据集中的错误标签数据.
  • 为了提高在这些清理数据集上训练的诊断模型的准确性和可靠性.
  • 提供一个不需要专家医生注释或额外的高质量标记数据集的解决方案.

主要方法:

  • MIDC框架利用同一疾病的多个公共数据集,利用不同的CNN进行自动图像识别和错误标签检测.
  • 实施了一项新的分级规则,将数据集分为高精度和低精度组.
  • 一个基于CNN的数据清理模块使用高精度数据集来识别和删除低精度数据集中的错误标签数据.

主要成果:

  • 该框架在四个不同的医学成像数据集上进行了测试:糖尿病视网膜病变,病毒性肺炎,乳腺瘤和皮肤癌.
  • 在所有测试的数据集中,平均诊断准确性在清洗后显著增加.
  • 对于糖尿病视网膜病变,准确度的改善范围为71.18%至85.13%,对于病毒性肺炎,准确度为82.50%至93.79%,对于乳腺瘤,准确度为85.59%至93.45%,对于皮肤癌,准确度为84.55%至94.21%.

结论:

  • 拟议的MIDC框架有效地自动化了医疗成像数据集中清除错误标记数据的过程.
  • 这种自动清洗显著提高了CNN模型的诊断准确性.
  • MIDC为改善公共医疗数据集用于疾病诊断的实用性提供了一个有价值的工具.