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提高注释效率,以完全标记乳腺质量细分数据集的注释效率.

Vaibhav Sharma1, Alina Jade Barnett1, Julia Yang1

  • 1Duke University, Department of Computer Science, Durham, North Carolina, United States.

Journal of medical imaging (Bellingham, Wash.)
|May 26, 2025
PubMed
概括

这项研究引入了一种主动学习 (AL) 框架,以有效地创建标记乳腺癌检测的乳腺扫描数据集. 该方法显著减少了对专家注释器输入的需求,加速了用于早期癌症诊断的AI模型的开发.

关键词:
积极学习是积极学习.癌症 癌症 癌症 癌症 癌症深度学习是一种深度学习.机器学习是机器学习.乳房学 乳房学 乳房学细分化 细分化的细分化

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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
  • 机器学习 机器学习

背景情况:

  • 乳腺癌是妇女死亡的主要原因,需要有效的早期检测.
  • 选计划的目标是早期检测,但标记图像数据集的短缺阻碍了AI模型的开发.
  • 改善用于乳腺癌检测的AI模型需要解决创建大型,准确标记数据集的瓶.

研究的目的:

  • 提出一个主动学习 (AL) 框架,用于对2D数字乳房造影中的乳房质量进行细分.
  • 为了减少专家注释者在创建标记的乳房图像数据集时所需的手工工作.
  • 出版一套新的细分乳腺质量的数据集,以促进人工智能驱动的早期检测研究.

主要方法:

  • 开发了一种主动学习 (AL) 框架,用于对乳腺图像群体的像素智能二进制细分.
  • 创建了一个由放射学家验证的1136个带有细分标签的乳房扫描质量的数据集.
  • 在AL框架内模拟了一个人类注释器,以训练和评估人工智能辅助的标签方法.

主要成果:

  • 通过积极学习框架,专家注释者的投入减少了44%.
  • 发表了一套1136个二进制细分标签的数据集,用于乳房学质量.
  • 在公共数据集上使用交叉与联合验证了拟议标签的质量.

结论:

  • 积极学习显著提高了创建标记的乳腺造影数据集的效率和时间效率.
  • 拟议的框架有助于开发高质量的数据集,并尽量减少人工工作.
  • 这项研究支持数字造乳镜和基于人工智能的乳腺癌查方面的进展.