一个大数据集的骨髓细胞在骨髓显样性综合征用于分类系统的数据集
Dan Shen1, Xiangli Gao1, Jixiang Tong1
1The First Affiliated Hospital of Zhejiang University Medical College, Hangzhou, China.
Scientific data
|November 20, 2025
概括
这项研究引入了大量的骨髓细胞图像数据集,用于诊断骨髓显形综合征 (MDS). 该数据集有助于开发人工智能模型,以准确识别失生细胞,改善血液恶性瘤诊断.
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
- 人工智能的人工智能
背景情况:
- 骨髓涂抹细胞形态学对于诊断血液恶性瘤至关重要.
- 骨髓发育综合征 (MDS) 由于细胞异常,对自动图像分析提出了复杂的挑战.
研究的目的:
- 为人工智能模型培训创建来自MDS患者骨髓细胞图像的综合数据集.
- 为了促进AI的发展,能够在MDS中分化失塑细胞.
主要方法:
- 从125名MDS患者的25009张骨髓细胞图像的数据集进行了策划.
- 图像被仔细标记为27个类别,包括正常和异常的MDS细胞 (例如微型巨核细胞),经过专家审查验证.
- 细胞图像被裁剪以确保形态完整性,边界框被平方化并扩展了10%.
主要成果:
- 该数据集使人工智能模型的训练成为可能,以提高MDS的诊断能力.
- 在这些数据上训练的人工智能模型可以实现区分失生细胞的能力.
结论:
- 这一数据集代表了在MDS中推进骨髓涂抹的自动化分析的重要资源.
- 改进的人工智能模型可以帮助准确和高效地诊断骨髓质疏松综合征.
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