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一个大规模的外周血细胞数据集用于自动化血液学分析
Atıf Eren Yarıkan1,2, Can Örer3, Volkan Akyıldız3
1Computer Engineering, Fatih Sultan Mehmet Vakıf University, İstanbul, Türkiye.
Scientific data
|February 6, 2026
概括
这项研究介绍了KU-Optofil PBC数据集,这是最大的外围血液细胞图像公开集合,用于自动分析. 它有助于开发先进的血液学系统,以准确分类白细胞.
科学领域:
- 血液学 血液学 血液学
- 医学图像分析 医学图像分析
- 计算生物学 计算生物学
背景情况:
- 准确的白细胞分类对于诊断血液学疾病至关重要.
- 现有的自动化血细胞分析数据集缺乏规模和多样性.
- 需要全面,高质量的数据集来推进血液学中的AI.
研究的目的:
- 介绍KU-Optofil PBC数据集,这是最大的外围血液细胞图像公开收集.
- 为13个不同的细胞类提供多样化的数据集,包括罕见的亚型.
- 促进开发强大的自动化血液学系统.
主要方法:
- 采集了31,489张高分辨率显微镜图像,这些图像是5月-格伦瓦尔德-吉姆萨染色的血液涂抹.
- 标准化成像条件使用Sysmex DI-60系统在100×放大.
- 专家血液学家的手册注释,具有较高的评价者间一致性 (科恩卡帕>0.85).
主要成果:
- KU-Optofil PBC 数据集包含13个细胞类的31,489张图像,包括常见和罕见的类型.
- 图像被组织成培训,验证和测试集 (70:10:20) 在368×368分辨率.
- 基线深度学习实验显示,DenseNet-121实现了95.23%的准确性,证明了数据集的实用性.
结论:
- KU-Optofil PBC 数据集解决了医疗图像分析资源中的关键缺口.
- 这一数据集支持对外围血液细胞分析的先进自动化系统的开发.
- 该资源预计将大大推进自动化血液学诊断和临床应用.
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