宫整体幻灯片图像数据集用于多类分类
Mahnaz Mohammadi1, Christina Fell1, David Morrison1
1School of Medicine, University of St Andrews, North Haugh, St Andrews, KY16 9TF, United Kingdom.
GigaScience
|November 29, 2025
概括
这项研究为机器学习引入了大量注释性宫活检图像的数据集. 这种资源使幻灯片的自动分析成为可能,改善了宫癌诊断和患者选.
科学领域:
- 数字病理学数字病理学
- 计算病理学计算病理学
- 机器学习在医疗保健中的应用
背景情况:
- 宫癌的诊断依赖于细胞学和活检样本的手动审查.
- 活检幻灯片的组织学检查给病理学家带来了相当大的工作负载.
- 缺乏大型的注释数据集阻碍了用于诊断辅助的AI工具的开发.
研究的目的:
- 创建和共享一个全面的数据集注释的宫活检全幻灯片图像.
- 促进用于宫癌诊断的机器学习算法的开发和验证.
- 解决计算病理学中有限的注释数据的障碍.
主要方法:
- 编制了一个由2539个从宫活检样本的全幻灯片图像组成的数据集.
- 图像由多位病理学家手动注释,对诊断和特征达成共识.
- 提供了Jason格式的注释,对应于iSyntax全幻灯片图像.
主要成果:
- 数据集包括在子幻灯片层面上的详细注释.
- 每个图像都被赋予一个共识诊断和子类别标签.
- 这一数据集因其规模和公众可访问的注释水平而独一无二.
结论:
- 该数据集使得开发一个准确的诊断预测模型成为可能.
- 活检样本的自动分类可以加快显著病理的识别.
- 本资源支持计算机视觉研究,用于人类组织诊断.
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