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电子染色DermaRepo:H&E整个幻灯片图像染色数据集

Muhammad Zeeshan Asaf1, Anum Abdul Salam1, Samavia Khan2,3

  • 1Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan.

Data in brief
|November 5, 2024
PubMed
概括

这项研究引入了一套新的皮肤活检图像数据集,用于人工智能 (AI) 疾病诊断. 双对比生成对抗网络 (GAN) 成功生成了现实的虚拟染色,提高了诊断效率.

关键词:
发光场显微镜 发光场显微镜组织学染色 组织学染色虚拟染色是一种虚拟的染色.整个幻灯片图像细分 整体幻灯片图像细分

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科学领域:

  • 数字病理学数字病理学
  • 医学成像医学成像
  • 医疗保健中的人工智能

背景情况:

  • 自动疾病诊断依赖于人工智能和机器学习框架的大型注释数据集.
  • 及时和准确的诊断对于改善患者的治疗结果至关重要.
  • 当前的方法往往需要大量的手动注释和医疗图像的处理.

研究的目的:

  • 开发一个全面的数据集的整个幻灯片图像,用于皮肤活检分析.
  • 为了几乎复制血素和乙 (H&E) 染色过程.
  • 提高计算机辅助诊断管道的效率和准确性.

主要方法:

  • 使用亮场显微镜创建了一个未染色的皮肤活检图像存储库.
  • 用化学和虚拟染色图像样本被生成用于比较.
  • 一个双对比的生成对抗网络 (GAN) 被训练来合成几乎染色的图像.

主要成果:

  • 经过训练的GAN在虚拟和化学染色图像之间获得了80.47的Fréchet Inception Distance (FID) 得分,这表明内容相关性很高.
  • 在未染色和几乎染色,未染色和化学染色图像之间,分别观察到FID分数为342.01和320.40.
  • 结果表明该模型能够产生现实的虚拟污点.

结论:

  • 开发的数据集和虚拟染色方法可以显著提高疾病诊断管道的效率.
  • 虚拟染色提供了一种有希望的方法,可以减少对手工染色程序的依赖.
  • 这项工作有助于在数字病理学中推进AI驱动的诊断工具.