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深度学习用于计算机辅助的数字乳房造影异常分类:以数据为中心的视角.

Vineela Nalla1, Seyedamin Pouriyeh1, Reza M Parizi2

  • 1Department of Information Technology, Kennesaw State University, Kennesaw, Georgia, USA.

Current problems in diagnostic radiology
|February 1, 2024
PubMed
概括
此摘要是机器生成的。

这项研究回顾了公共乳腺扫描数据集,这对训练深度学习 (DL) 模型进行乳腺癌检测至关重要. 它提供了一个改善自主诊断和模型比较的资源.

关键词:
乳腺癌 乳腺癌 乳腺癌癌症查 癌症查深度学习 (Deep Learning) 是一种深度学习.全场数字乳房造影 (FFDM) 是指全场数字乳房造影.乳房学 乳房学 乳房学公开的数据集是公开的.

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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
  • 在瘤学瘤学.

背景情况:

  • 乳腺癌是女性癌症的主要原因,通过乳房扫描早期检测可以提高存活率.
  • 深度学习 (DL) 模型需要不同的数据集来准确的自主乳腺癌诊断.
  • 公共乳房造影数据集的有限可用性阻碍了DL模型开发和比较分析.

研究的目的:

  • 为了全面描述和审查目前可用的公共乳房造 mammography 数据集.
  • 为乳腺癌研究人员和从业人员提供一个有价值的资源DL.
  • 促进开发和评估更有效的DL模型用于乳腺癌检测.

主要方法:

  • 对公开可访问的乳房学数据集进行系统审查和描述.
  • 分析数据集特征和可用于DL模型培训的可用性.
  • 关于现有的公共乳房造影数据集信息的总结.

主要成果:

  • 描述了一组精选的公共乳房学数据集.
  • 审查了这些数据集在DL应用中的可用性.
  • 确定了DL模型数据集可用性和可比性方面的挑战.

结论:

  • 公共的乳房造影数据集对于推动乳腺癌诊断中的DL至关重要.
  • 这项工作通过整合可用的数据集信息来弥合知识差距.
  • 更好地理解和利用这些数据集将加强DL模型的开发和验证.