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

Characteristic and endovascular management of peripancreatic arterial aneurysms: Experience from a single-center cohort study.

Science progress·2026
Same author

Engineered probiotics for tumor-targeted combination chemoimmunotherapy.

bioRxiv : the preprint server for biology·2026
Same author

pH-responsive polymeric-peptide complex enhances tumor immunogenic cell death by membrane disruption.

Journal of controlled release : official journal of the Controlled Release Society·2026
Same author

Using Flight Plan for Embolization Technique Based on Carotid Body Tumor Angiography to Improve Surgical Treatment of the Lesion: Report of Two Cases.

Head & neck·2025
Same author

Gender-based differences of intraoperative transfusion during open surgery for descending thoracic and abdominal aortic aneurysms: a retrospective single-center cohort study.

BMC surgery·2025
Same author

A multi-modal dataset of electroencephalography and functional near-infrared spectroscopy recordings for motor imagery of multi-types of joints from unilateral upper limb.

Scientific data·2025

相关实验视频

Updated: Jul 16, 2025

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research
05:22

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Published on: June 21, 2024

438

一个拉普拉斯金字塔基于生成的H&E污点增强网络.

Fangda Li, Zhiqiang Hu, Wen Chen

    IEEE transactions on medical imaging
    |September 19, 2023
    PubMed
    概括

    生成性斑点增强网络 (G-SAN) 使用一种新的方法来模拟组织学图像中的现实的斑点变化. 这种方法通过提高概括性和准确性来增强医疗诊断的机器学习模型.

    科学领域:

    • 数字病理学数字病理学
    • 计算生物学 计算生物学
    • 医疗成像医学成像

    背景情况:

    • 血素和乙 (H&E) 染色对于医学诊断中的组织学图像分析至关重要.
    • 在H&E图像中的污点变化对机器学习模型概括提出了重大挑战.
    • 目前的方法很难解释染色试剂和协议的变化.

    研究的目的:

    • 开发一种增强组织学图像的方法,以现实的染色变化.
    • 提高计算机辅助诊断的机器学习模型的概括能力.
    • 为了使诊断模型对H&E染色的变化不敏感.

    主要方法:

    • 提出了生成污点增强网络 (G-SAN),这是一个基于生成对抗网络 (GAN) 的框架.
    • 利用基于拉普拉斯金字塔 (LP) 的计算效率高的发电机架构.
    • 在生成的图像中,从细胞形态中解脱出斑点的特征.

    主要成果:

    • G-SAN成功地为组织学图像生成了现实的染色变化.
    • 使用G-SAN增强数据的训练模型改善了F1补丁分类得分,平均为15.7%.
    • 在使用G-SAN增强数据的全视质量中,核细分性能提高了7.3%.

    更多相关视频

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

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

    Published on: February 15, 2017

    7.0K
    Analyzing Dendritic Morphology in Columns and Layers
    08:41

    Analyzing Dendritic Morphology in Columns and Layers

    Published on: March 23, 2017

    9.4K

    相关实验视频

    Last Updated: Jul 16, 2025

    Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research
    05:22

    Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

    Published on: June 21, 2024

    438
    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

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

    Published on: February 15, 2017

    7.0K
    Analyzing Dendritic Morphology in Columns and Layers
    08:41

    Analyzing Dendritic Morphology in Columns and Layers

    Published on: March 23, 2017

    9.4K

    结论:

    • G-SAN有效地解决了组织学图像中斑点变异性的挑战.
    • 拟议的方法提高了基于机器学习的诊断工具的稳定性和准确性.
    • G-SAN提供了一种有价值的方法来改进数字病理学工作流程.