Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

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

Personalized multi-agent reinforcement learning framework for adaptive chronic disease therapy management.

Scientific reports·2026

Same author

Studying Dynamical Characteristics of Oxygen Saturation Variability Signals Using Haar Wavelet.

Healthcare (Basel, Switzerland)·2023

Same author

Al-Biruni Earth Radius Optimization with Transfer Learning Based Histopathological Image Analysis for Lung and Colon Cancer Detection.

Cancers·2023

Same author

Improved Wireless Medical Cyber-Physical System (IWMCPS) Based on Machine Learning.

Healthcare (Basel, Switzerland)·2023

Search research articles

相关实验视频

Updated: Jul 12, 2025

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

用混沌蝶优化算法对线粒细胞核进行细分和分类,并对基因病理学图像进行深度学习.

Rayed AlGhamdi¹

¹Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Biomimetics (Basel, Switzerland)

|October 27, 2023

概括

这项研究引入了一种用于在基因病理图像中自动化分离和分类线粒细胞核的新方法. 该技术通过使用深度学习和优化算法准确识别细胞分裂阶段,提高了癌症诊断和预后.

科学领域:

* 计算机病理学
* 医学图像分析
* 在瘤学中的机器学习.

背景情况:

* 组织病理学分类对于瘤评估和个性化治疗至关重要.
* 线粒细胞核的分类有助于癌症的诊断和预后,但由于尺寸小和外观多样,具有挑战性.
*使用计算机视觉 (CV) 和机器学习 (ML) 的自动化方法对于准确识别线粒细胞核至关重要.

研究的目的:

* 引入一种用于线粒细胞核细分和分类的自动化技术.
* 为了提高在组织病理学图像中识别正在发生神经分裂的细胞的准确性.
*通过精确的线粒细胞核检测,改善癌症诊断和预后.

主要方法:

* 提出了使用混乱蝶优化算法与深度学习 (MNSC-CBOADL) 技术进行线粒细胞核细分和分类.
* 利用U-Net模型进行图像分割和Xception模型进行特征向量生成.
* 采用深度信念网络 (DBN) 进行分类,使用混乱蝶优化算法 (CBOA) 进行超参数调整.

主要成果:

*MNSC-CBOADL技术在自动化线粒细胞核细分和分类方面表现出卓越的性能.
*使用基准数据库的验证证实了拟议系统的有效性.

关键词:

计算机辅助诊断是指计算机辅助的诊断.深度学习是一种深度学习.这是一种超听证学 (metaheuristics).线粒细胞核的分类细分化细分化的细分化

更多相关视频

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

相关实验视频

Last Updated: Jul 12, 2025

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

* 深度学习和优化算法的综合方法显著提高了检测准确性.

结论:

*MNSC-CBOADL技术为自动化线粒细胞核分析提供了强大的解决方案.
*这种方法有可能在癌症诊断和治疗规划中显著帮助病理学家.
*先进的深度学习和优化策略是克服基因病理图像分析挑战的关键.