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Classification of Leukocytes01:30

Classification of Leukocytes

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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
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Updated: Jan 10, 2026

Enumeration of Major Peripheral Blood Leukocyte Populations for Multicenter Clinical Trials Using a Whole Blood Phenotyping Assay
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可解释的多个实例学习用于从外周血液涂抹的血液诊断.

Siddharth Singi1, Shenghuan Sun2, Zhanghan Yin1,3

  • 1Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

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概括
此摘要是机器生成的。

我们开发了CAREMIL,这是一个新的弱监督框架,用于从外周血液涂抹中诊断血液癌症. 它整合了细胞形态和组成,用于准确,全幻灯片预测,优于现有方法.

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科学领域:

  • 血液学 血液学 血液学
  • 计算病理学计算病理学
  • 人工智能在医学中的应用

背景情况:

  • 精确诊断血液恶性瘤需要整合细胞形态和组成从周围血液涂抹 (PBSs).
  • 目前的自动化方法专注于单细胞分类,缺乏整片诊断能力.
  • 需要先进的计算工具来提高血液学中的诊断准确性和效率.

研究的目的:

  • 开发和评估一个新的弱监督框架,CAREMIL (细胞聚合,可解释,多个实例学习),用于全片血液恶性瘤诊断.
  • 将高性能基于细胞的编码器 (DeepHeme) 与CAREMIL集成,以实现强大的特征提取和诊断预测.
  • 评估CAREMIL的性能与现有的多实例学习 (MIL) 聚合功能和图像编码器相比.

主要方法:

  • 利用DeepHeme,一个基于细胞的执行编码器,从PBS图像中提取特征.
  • 开发了CAREMIL,一个基于注意力的MIL框架,使用弱监督学习来进行全幻灯片分类.
  • 评估了各种图像编码器和MIL架构,比较CAREMIL与封闭的MIL用于幻灯片级聚合.

主要成果:

  • 在DeepHeme和CAREMIL的组合中,在急性白血病 (AML),髓囊性综合征 (MDS) 和毛状细胞白血病 (HCL) 中实现了最高的诊断性能 (AUROCs 0.999,0.891,0.945).
  • 与封闭的MIL相比,CAREMIL作为聚合函数表现出了优越的性能,特别是与外域编码器 (如ImageNet,UNI2和Virchow2.2) 相比.
  • 该框架成功地识别了AML,即使在循环爆炸最小或不存在的情况下,也成功地识别了AML,突出了其敏感性.
  • 在CAREMIL中,注意力机制通过突出显示诊断相关的细胞和揭示疾病特异性的形态特征,提供了生物解释性.

结论:

  • 与DeepHeme相结合,CAREMIL代表了一种强大且可解释的MIL框架,用于血液学幻灯片诊断.
  • 该框架对单元级错误分类具有稳定性,并且不需要单元级明确的监督.
  • 卡雷米尔显示出适用于其他液体活检标本的潜力,并支持转向血液学中的定量,形态学知情诊断.