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

Allosteric coupling of RyR calcium channels: Is it relevant to the [patho]physiology of heart and muscle?

The Journal of general physiology·2025
Same author

Artificial intelligence approaches to the volumetric quantification of glycogen granules in EM images of human tissue.

The Journal of general physiology·2024
Same author

Muscle calcium stress cleaves junctophilin1, unleashing a gene regulatory program predicted to correct glucose dysregulation.

eLife·2023
Same author

Excitation-contraction coupling in cardiac, skeletal, and smooth muscle.

The Journal of general physiology·2022
Same author

A novel method for determining murine skeletal muscle fiber type using autofluorescence lifetimes.

The Journal of general physiology·2022
Same author

A chloride channel blocker prevents the suppression by inorganic phosphate of the cytosolic calcium signals that control muscle contraction.

The Journal of physiology·2020

相关实验视频

Updated: Jun 12, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K

在一般生理学中使用神经网络进行图像分析.

Eduardo Rios1

  • 1Department of Physiology and Biophysics, Rush University, Chicago, IL, USA.

The Journal of general physiology
|September 17, 2024
PubMed
概括

本文解释了用于生物图像分析的卷积神经网络 (CNN),并指导研究人员应用免费可用的机器学习 (ML) 工具. 它澄清了最近的网络描述,以实现更广泛的访问性.

科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 机器学习应用 机器学习应用

背景情况:

  • 生物图像分析带来了复杂的挑战.
  • 机器学习 (ML) 为生物数据解释提供了强大的工具.
  • 卷积神经网络 (CNN) 是用于图像分析的关键ML技术.

研究的目的:

  • 介绍生物图像分析CNN的基本概念.
  • 为在生物学研究中实施ML工具提供实用指南.
  • 为了增强对最近生物研究中使用的特定CNN架构的理解.

主要方法:

  • 审查CNN核心原则及其与生物成像相关性.
  • 探索可访问的,开源的ML库和框架.
  • 详细分析和澄清来自Ríos等的CNN模型. (2024年) 的时间.

主要成果:

  • 对生物图像处理CNN的基本理解.
  • 在研究环境中采用和调整ML工具的路线图.
  • 提高了复杂的CNN架构的清晰度和逻辑解释.

结论:

更多相关视频

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates
10:18

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates

Published on: July 9, 2020

2.9K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

相关实验视频

Last Updated: Jun 12, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K
Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates
10:18

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates

Published on: July 9, 2020

2.9K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K
  • 生物图像分析的进步中,CNN变得越来越重要.
  • 可访问的ML工具可以加速生物发现.
  • 更清晰的解释先进的方法促进更广泛的采用和创新.