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The hematopoietic stem cells or HSCs are multipotent, meaning they can differentiate and give rise to all blood and immune cells. HSCs are maintained in the quiescent stage until an external stimulus initiates their differentiation. The multipotent HSCs exist as two heterogeneous populations, long-term repopulating cells (LTRC) and short-term repopulating cells (STRC). The two HSC populations have different surface markers or receptors and are classified based on quiescence and long-term...
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可转移的自动血液细胞分类:通过自我监督学习克服数据限制.

Laura Wenderoth1, Anne-Marie Asemissen2, Franziska Modemann2

  • 1Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Christoph-Probst-Weg 1, 20251 Hamburg, Germany; Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany; Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany.

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自主监督学习 (SSL) 有效地从血液细胞图像中提取特征,没有标签. SSL模型在分类外围血液细胞方面表现出卓越的表现,即使具有有限的标记数据,也优于传统方法.

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细胞分类 细胞分类域名适应 域名适应域名转移 域名转移 域名转移标签效率 标签的效率 标签的效率在白血病中,白血病.自主监督学习学习

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科学领域:

  • 血液学 血液学 血液学
  • 计算生物学 计算生物学
  • 机器学习 机器学习

背景情况:

  • 精确分类周围血液和骨髓细胞对于诊断和监测血液学疾病至关重要.
  • 目前的自动分类系统面临的挑战是由于数据稀缺性和不同实验室的有限通用性.
  • 自主监督学习 (SSL) 提供了一种有希望的方法来克服细胞分类中的这些局限性.

研究的目的:

  • 将SSL集成到血液学疾病的细胞分类管道中.
  • 在自动化细胞分类中应对数据稀缺性和模型通用性的挑战.
  • 与监督方法相比,评估基于SSL的特征提取和分类的性能.

主要方法:

  • 利用了四个公共的血液学单细胞图像数据集 (一个骨髓,三个外周血液).
  • 采用基于SSL的方法来提取图像特征,不需要最初的图像注释.
  • 应用了一种轻量级的机器学习分类器,使用SSL功能对一小部分注释图像进行训练.

主要成果:

  • 与监督深度学习模型相比,在骨髓数据上训练的SSL模型在转移到外围血液数据集时表现出更高的分类准确性.
  • 在每类微调50个标记样本后,SSL管道在特定数据集和罕见细胞类型的监督深度学习中表现出色.
  • 在其他数据集上,SSL方法显示了与监督方法相比的性能.

结论:

  • 通过SSL,可以在不依赖类标签的情况下提取重要的细胞图像特征.
  • 骨髓和外围血液细胞领域之间的知识传输是SSL有效地促进的.
  • SSL模型展示了有效的适应新数据集与最小的标记数据,提高分类准确性.