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Classification of Leukocytes

Classification of Leukocytes

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: Jul 27, 2025

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

MM-GLCM-CNN:一个基于多个尺度和多个层次的GLCM-CNN用于聚合物分类.

Shu Zhang¹, Jinru Wu¹, Enze Shi¹

¹Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an 710000, China.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

|June 10, 2023

概括

这项研究引入了一种新的基于多尺度和多层次的灰色水平共发生矩阵CNN (MM-GLCM-CNN),用于区分恶性和良性息肉. MM-GLCM-CNN有效地利用来自小型数据集的纹理特征,在损伤分类中实现高精度.

关键词:

深度学习是一种深度学习.灰色水平同时发生矩阵.多层次的多层次的多个尺度的多个尺度.聚类的分类方法小数据集是一个小的数据集.

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

相关实验视频

Last Updated: Jul 27, 2025

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

区分恶性和良性病变对于有效的癌症管理和早期检测至关重要.
卷积神经网络 (CNN) 在医学成像中表现有前途,但需要大量的数据集,这些数据集通常无法用于病理基础真相.
小型,病理证明的数据集对训练准确的CNN模型进行病变诊断构成了挑战.

研究的目的:

开发一种CNN模型,能够从小型,病理证明的数据集中学习特征,以区分恶性和良性结肠多.
通过结合多尺度和多层次的纹理分析来增强特征提取.
利用有限的数据,提高CNN在病变分类中的诊断性能.

主要方法:

提出了一个基于多个尺度和多个层次的灰色层次共发生矩阵CNN (MM-GLCM-CNN) 模型.
利用灰色级别的同时发生矩阵 (GLCM) 来描述病变的异质性和纹理.
实现了一个自适应的多输入CNN框架与一个适应的重量网络的功能融合和改进.

主要成果:

在MM-GLCM-CNN的小结肠多重体数据集上,ROC曲线下的面积 (AUC) 达到93.99%.
在同一数据集上,与现有最先进的方法相比,表现出1.49%的性能提升.
验证了在有限的数据基础上将病变特征异质性纳入恶性瘤预测的有效性.

结论:

MM-GLCM-CNN模型成功地使用小数据集的纹理特征来区分恶性和良性息肉.
该方法强调了病变异质在改善有限的病理数据的诊断准确性方面的重要性.
这种方法为基于CNN的病变诊断提供了可行的解决方案,当大型注释数据集不可用时.