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

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

Out of Distribution Generalization via Interventional Style Transfer in Single-Cell Microscopy.

Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops·2026
Same author

Otovent Versus Valsalva: Physiological Insights for Diagnostic and Therapeutic Autoinflation in Eustachian Tube Dysfunction.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery·2026
Same author

Pilot-Scale Evaluation of Partial-Slide Imaging for Detecting Critical Morphological Features (Excluding Parasites): Initial UK Implementation of a Hybrid Virtual-Light Microscopy Model in Haematology.

International journal of laboratory hematology·2026
Same author

Epidural anesthesia not associated with decreased 30-day surgical site infection occurrence after open colorectal surgery.

Antimicrobial stewardship & healthcare epidemiology : ASHE·2026
Same author

Analysis of Patient Presentation to the ED within 90-days of Infra-inguinal Bypass.

Annals of vascular surgery·2026
Same author

Presaccadic suppression is reduced for antisaccades.

Journal of neurophysiology·2026

相关实验视频

Updated: Jul 2, 2025

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

17.3K

基于图像的扰动概况的学习表示.

Nikita Moshkov1, Michael Bornholdt2, Santiago Benoit2,3

  • 1HUN-REN Biological Research Centre, 62 Temesvári krt, Szeged, 6726, Hungary.

Nature communications
|February 21, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了Cell Painting CNN,一种用于分析细胞成像数据的计算方法. 它提高了在细胞生物学研究中识别治疗效果的准确性和效率.

更多相关视频

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K
Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training
06:20

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

Published on: December 6, 2024

2.8K

相关实验视频

Last Updated: Jul 2, 2025

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

17.3K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K
Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training
06:20

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

Published on: December 6, 2024

2.8K

科学领域:

  • 细胞生物学 细胞生物学
  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 高通量成像试验对于研究细胞生物学至关重要,并且需要计算方法来进行数据分析.
  • 从图像中量化治疗对细胞表型的影响对于生物发现至关重要.

研究的目的:

  • 开发一种改进的策略,利用因果解释,从高通量成像数据中学习治疗效应的表征.
  • 创建一个可重复使用的卷积神经网络 (CNN),用于基于图像的分析.

主要方法:

  • 利用弱监督学习来建模细胞图像和治疗之间的关联.
  • 从五项研究中构建了一个多样化的训练数据集,以最大限度地提高实验变异性,并促进混杂因素和表型特征的分离.
  • 开发了细胞绘画CNN模型.

主要成果:

  • 细胞绘画CNN成功地编码了其学习的表征中的混因素和表型特征.
  • 使用多样化的数据集进行培训,提高了下游分析的性能.
  • 与经典特征相比,细胞绘画CNN在下游分析性能上表现出高达30%的改进.
  • 细胞绘画CNN在计算上比传统方法更有效.

结论:

  • 拟议的策略和Cell Painting CNN为细胞生物学中的基于图像的分析提供了一个更准确,更高效的计算方法.
  • 这种可重复使用的CNN可以推进用于药物发现和生物研究的大规模成像数据集的分析.