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

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

1.2K
Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
1.2K

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

Recent advances in polysaccharopeptides from Trametes versicolor: Extractions, structural characteristics, bioactivities, and applications: A review.

International journal of biological macromolecules·2026
Same author

Post-marketing safety evaluation of anthracycline for acute myeloid leukemia treatment: a real-world pharmacovigilance analysis.

Frontiers in pharmacology·2026
Same author

SketchBodyNet++: Sketch-Based 3D Human Mesh Reconstruction Via Hybrid Parametric Networks.

IEEE transactions on visualization and computer graphics·2026
Same author

Ebselen's role in overcoming cisplatin resistance in colorectal Cancer via SQSTM1 ubiquitination modulation.

International immunopharmacology·2026
Same author

Noninvasive prediction of coronary artery disease progression using pericoronary adipose tissue radiomics from coronary CTA.

The British journal of radiology·2026
Same author

Variable effects of biochar on soil greenhouse gas emissions: A meta-analysis of climate, soil, and biochar property interactions.

Environmental research·2026

相关实验视频

Updated: May 6, 2026

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

9.7K

基于伪标签纠正和不确定性否定的弱监督的核细分.

Xipeng Pan1, Shilong Song1, Zhenbing Liu1

  • 1Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, 541004, Guangxi, China.

Artificial intelligence in medicine
|April 2, 2025
PubMed
概括

这项研究引入了一种新的两阶段弱监督模型,用于仅使用点注释的组织病理图像中的核细分. 与现有的以点标签为基础的方法相比,该方法实现了更高的性能.

关键词:
核心细分的核心细分.点的注释点的注释伪标签拒绝使用.监督的弱点 监督的弱点

更多相关视频

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.4K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K

相关实验视频

Last Updated: May 6, 2026

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

9.7K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.4K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K

科学领域:

  • 数字病理学数字病理学
  • 医疗图像分析 医学图像分析
  • 计算生物学是一种计算生物学.

背景情况:

  • 精确的细胞核细分对于计算机辅助组织病理学至关重要,但手动注释是繁的.
  • 现有的完全监督的方法需要精确的像素级注释,这些注释需要很长时间才能获得.

研究的目的:

  • 开发一个两阶段的弱监督模型,用于仅使用点注释的核细分.
  • 为了提高整个幻灯片图像中核细分的效率和准确性.

主要方法:

  • 一个采用粗细分段阶段的两阶段模型.
  • 使用沃罗诺伊图和K-means集群进行初始监督.
  • 包含一个图像自适应集群伪标签算法和多尺度特征融合 (MFF) 模块.
  • 采用指数移动平均线用于集群标签 校正 (EMAC) 和不确定性估计用于 denoising.

主要成果:

  • 拟议的方法在MoNuSeg和TNBC公开基准上取得了卓越的表现.
  • 在仅使用点注释的核心细分方面表现出有效性.
  • 优于依赖点标签的现有核细分方法.

结论:

  • 开发的弱监督模型为核细分提供了高效和准确的解决方案.
  • 点注释足以训练一个高性能细分模型.
  • 该方法对自动化组织病理学分析具有重大意义.