Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Classification of Leukocytes01:30

Classification of Leukocytes

2.0K
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...
2.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Erratum: Combination of time series forecasting models with a microscopic and stochastic approach to predict road traffic noise [J. Acoust. Soc. Am. 159(3), 2754-2778 (2026)].

The Journal of the Acoustical Society of America·2026
Same author

Combination of time series forecasting models with a microscopic and stochastic approach to predict road traffic noise.

The Journal of the Acoustical Society of America·2026
Same author

Sector Classification of Unerupted Maxillary Canines: A Deep Learning-Based Automated Framework Using Panoramic Radiographs.

Orthodontics & craniofacial research·2026
Same author

miRNA Cell Tracer: Multifunctional Microgels for Spatially Resolved and Wide-Range Detection of Intracellular miRNA at Single-Cell Level.

ACS sensors·2026
Same author

Material-Induced Nuclear Deformation Controls Chromatin Architecture in Adipose Stem Cells.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Single-cell three-dimensional tracking by means of neural networks for sperm rolling classification.

Journal of the Royal Society, Interface·2026

相关实验视频

Updated: Jul 12, 2025

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence
06:56

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence

Published on: April 12, 2024

624

通过基于神经网络的散射快照识别来区分未知的细胞类别.

Gaia Cioffi1, David Dannhauser1, Domenico Rossi2

  • 1Interdisciplinary Research Centre on Biomaterials (CRIB) and Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli "Federico II", Piazzale Tecchio 80, 80125 Naples, Italy.

Biomedical optics express
|October 19, 2023
PubMed
概括

这项研究引入了一个开放式的神经网络,用于在生命科学中分类未知的细胞类型. 该方法精确检测未知细胞并量化预测不确定性,改进深度学习应用程序.

更多相关视频

Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy
07:29

Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy

Published on: May 27, 2020

2.8K
Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.2K

相关实验视频

Last Updated: Jul 12, 2025

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence
06:56

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence

Published on: April 12, 2024

624
Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy
07:29

Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy

Published on: May 27, 2020

2.8K
Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.2K

科学领域:

  • 生命科学 生命科学
  • 生物技术是生物技术.
  • 计算生物学 计算生物学

背景情况:

  • 神经网络图像分类在生命科学中至关重要,但与未知数据作斗争.
  • 当前的封闭集模型错误地分类未知图像,需要先进的方法.
  • 开放集分类通过区分已知的和未知的数据分布提供了一个解决方案.

研究的目的:

  • 实施和评估活细胞图像分析的开放集分类方法.
  • 区分已知的单细胞细胞类和未知的瘤细胞系.
  • 评估实验错误的影响,并优化神经网络超参数用于未知细胞检测.

主要方法:

  • 应用了一个开放式神经网络框架来分散活细胞的快照.
  • 针对四个已知的单细胞细胞类和一个未知的瘤单细胞细胞系.
  • 研究了实验样本错误和优化了神经网络超参数.

主要成果:

  • 开放式方法成功地区分了已知的和未知的细胞类.
  • 在检测未知的瘤细胞系方面取得了高精度.
  • 证明了对实验样本噪声的强度.
  • 神经网络揭示了细胞预测中的测量不确定性.

结论:

  • 开放式分类对于在生命科学成像中识别未知的细胞类型是有效的.
  • 开发的方法对实验噪声具有强度,这是生物应用的关键要求.
  • 该方法为单细胞分类的预测不确定性提供了有价值的见解.
  • 这个框架在各种单细胞分析任务中具有广泛的适用性.