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相关概念视频

Flow Cytometry01:23

Flow Cytometry

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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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可解释的无监督学习能够通过高通量成像流细胞计进行准确的集群.

Zunming Zhang1, Xinyu Chen1, Rui Tang2

  • 1Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA.

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|November 23, 2023
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概括

这项研究引入了一种无监督的深度学习模型,用于分析无标签成像流细胞计数据. 基于深度卷积自编码器的集群模型有效地在没有先前标签的情况下集群细胞,使高通量细胞分析的新见解成为可能.

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科学领域:

  • 计算生物学 计算生物学
  • 生物医学成像技术 生物医学成像技术
  • 机器学习 机器学习

背景情况:

  • 高通量成像流细胞计 (IFC) 产生了庞大的数据集,这给分析带来了挑战,特别是当地面真相标签不可用时.
  • 需要无监督学习方法来从没有标签的IFC数据中提取有意义的信息,而无需先前的生物知识.

研究的目的:

  • 开发和评估一个无监督的深度嵌入算法,用于集群无标签的IFC图像.
  • 证明模型在识别不同细胞群中的能力,包括那些具有微妙或非人类可识别特征的细胞群.

主要方法:

  • 实现基于深度卷积自编码器的集群 (DCAEC) 模型,用于无监督学习.
  • 将IFC图像编码为潜伏表示,用于随后的集群.
  • 使用梯度加权类激活映射 (Grad-CAM) 来解释模型识别的特征.

主要成果:

  • 使用3DIFC数据,实现了人类白细胞 (WBC) 聚类 (91.9%) 和WBC/白血病分类 (97.9%) 的高平衡精度.
  • 从无标签的2D传输和3D侧散射图像中证明了成功的集群 (85.3%的平衡精度),揭示了非人类可识别的模式.
  • 梯度加权类激活映射确定了突出的,集群特定的视觉模式,有助于解释神经网络特征识别.

结论:

  • DCAEC模型提供了一种有效的无监督方法来分析复杂的,无标签的IFC数据.
  • 该方法可以发现生物相关的细胞群,即使特征对人类来说是看不见的.
  • 这项工作是迈向使用IFC进行高通量细胞分析的可解释深度学习的重要一步.