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

A comprehensive European Colorectal Cancer Cohort dataset.

Scientific data·2026
Same author

Cell Invasion Analysis of Tumor Spheroids Using 2D Image Data.

ACS measurement science au·2026
Same author

From slides to AI-ready maps: Standardized multi-layer tissue maps as metadata for artificial intelligence in digital pathology.

Artificial intelligence in medicine·2026
Same author

Privacy-preserving data quality assessment for federated health data networks.

BMC medical informatics and decision making·2026
Same author

Definitions to data flow: Operationalizing MIABIS in HL7 FHIR.

Journal of biomedical informatics·2025
Same author

Genomic data sharing in research across Europe: legal challenges and upcoming opportunities within the European Health Data Space.

European journal of public health·2025

相关实验视频

Updated: Sep 12, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

在数字病理学中的机器学习.

Tomáš Brázdil, Vít Musil, Karel Štěpka

    Ceskoslovenska patologie
    |August 5, 2025
    PubMed
    概括
    此摘要是机器生成的。

    数字病理学使用机器学习 (ML) 和人工智能 (AI) 进行诊断. 本概述涵盖数据处理,挑战和软件解决方案,以加速这些学习系统的临床采用.

    关键词:
    数字病理学数字病理学图像处理 图像处理整个幻灯片图像的图像.人工智能的人工智能是人工智能.机器学习是机器学习.

    更多相关视频

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
    08:20

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

    Published on: October 27, 2023

    1.7K
    Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
    05:33

    Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

    Published on: July 11, 2025

    247

    相关实验视频

    Last Updated: Sep 12, 2025

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
    07:15

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

    Published on: August 16, 2020

    6.9K
    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
    08:20

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

    Published on: October 27, 2023

    1.7K
    Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
    05:33

    Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

    Published on: July 11, 2025

    247

    科学领域:

    • 数字病理学数字病理学
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 病理学的数字化正在迅速推进.
    • 机器学习 (ML) 和人工智能 (AI) 系统的临床实施落后于研究.
    • 需要弥合ML/AI开发与病理学临床实践之间的差距.

    研究的目的:

    • 提供关于在数字病理学中开发和部署学习系统的全面概述.
    • 解决处理数字病理学数据的技术挑战和潜在陷.
    • 概述ML/AI在病理学方面的当前和未来发展.

    主要方法:

    • 数字病理学数据特征的描述 (扫描仪,扫描,存储,传输,质量控制,注释).
    • 对查看数字幻灯片和使用学习系统实施诊断程序的软件解决方案的审查.
    • 解释常见任务,大型扫描的ML方法修改和诊断应用.

    主要成果:

    • 介绍了数字病理学数据处理中的技术挑战的当前方法.
    • 突出了数据处理中的潜在陷.
    • 概述了包含学习系统的现有软件和诊断应用程序.

    结论:

    • 了解数字病理学数据的细微差别对于成功实施ML/AI至关重要.
    • 应对技术挑战可以促进学习系统的采用.
    • 未来的发展包括大型基础模型和虚拟染色,有望进一步进步.