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

[Experimental study of the eyelid reconstruction in situ with the acellular xenogeneic dermal matrix].

Zhonghua zheng xing wai ke za zhi = Zhonghua zhengxing waike zazhi = Chinese journal of plastic surgery·2007
Same author

[Mutation analysis of GCH1 gene in Chinese patients with dopa responsive dystonia].

Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics·2007
Same author

[Screening and characterization of marine bacteria with antibacterial and cytotoxic activities, and existence of PKS I and NRPS genes in bioactive strains].

Wei sheng wu xue bao = Acta microbiologica Sinica·2007
Same author

[Collateral supply in patients with severe carotid stenosis].

Zhonghua yi xue za zhi·2007
Same author

[Changes of sleep architecture in patients with narcolepsy].

Zhonghua yi xue za zhi·2007
Same author

[Combined anterior and posterior approach for cervical fracture-dislocation with ankylosing spondylitis].

Zhonghua wai ke za zhi [Chinese journal of surgery]·2007

相关实验视频

Updated: May 29, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

3.9K

实体级多个实例学习用于美索斯科普基因病理图像分类与贝叶斯协作学习和病理先前转移.

Qiming He1, Yingming Xu1, Qiang Huang2

  • 1Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
|February 8, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了对美索斯科普基因病理图像的实体级多个实例学习. 这种新的方法准确地识别了23种病变类型,通过捕捉关键的病理特征和与较少实例的关系,超过了现有的方法.

关键词:
贝叶斯协作学习是贝叶斯的协作学习.淋巴细胞损伤的模式混合物 混合物 混合物多个实例的学习是多个实例的学习.病理学 病理学 病理学

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

1.3K
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

6.9K

相关实验视频

Last Updated: May 29, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

3.9K
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

1.3K
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

6.9K

科学领域:

  • 组织病理学 组织病理学
  • 计算病理学计算病理学
  • 机器学习 机器学习

背景情况:

  • 病理结构存在于中视尺度,由于有限的实例,对传统的多个实例学习构成挑战.
  • 这种限制阻碍了对局部特征及其关系的感知,导致语义模糊性和低效的实体嵌入.

研究的目的:

  • 开发一种新的实体级多个实例学习框架,以改进美索斯科普组织病理图像分类.
  • 在实体嵌入中解决有限实例和语义模糊性的挑战.

主要方法:

  • 提出了一种新的实体级多实例学习方法.
  • 实现实体组件混合,以更好地捕捉局部病理特征.
  • 利用贝叶斯协作学习来共同优化实例和袋子嵌入.
  • 应用病理先前转移用于全球关注聚合的初始优化.

主要成果:

  • 在质细胞图像数据集中,在23种病变类型中,在19种病变类型中实现了最先进的性能.
  • 20种类型的AUC超过90%,11种类型的AUC超过95%.
  • 与缩略图级和幻灯片级方法相比,显示了显著的改进 (高达18.9%和14.7%).
  • 废除研究证实了特征表示的协同增强,其实例较少.

结论:

  • 拟议的实体级多个实例学习框架能够准确地对23种病变模式进行分类.
  • 该方法有效地从有限的实例中捕捉出突出的病理特征和上下文关系.
  • 这种方法为组织病理学图像分类提供了一个有希望的工具,可以扩展到其他病理学实体.