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

Prosopagnosia01:24

Prosopagnosia

1.3K
Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
1.3K

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
Same journal

Corrigendum to "Integrative adaptive indexes from noisy routine haematological markers can predict and discriminate health status and biological age" [Comput. Biol. Med. 208 (2026) 111628].

Computers in biology and medicine·2026
Same journal

Fluid dynamics-informed CCTA-derived geometric parameters in right coronary artery anomalies predict abnormal invasive Adenosine-FFR and Dobutamine-FFR.

Computers in biology and medicine·2026
Same journal

Corrigendum to "CFPNet-M: A light-weight encoder-decoder based network for multimodal biomedical image real-time segmentation" [Comput. Biol. Med. 154 (2023) 106579].

Computers in biology and medicine·2026
Same journal

ECG arrhythmia classification via wavelet-driven feature extraction and swarm-optimised gradient boosting.

Computers in biology and medicine·2026
Same journal

Electro-osmotic metachronal cilia transport of viscoelastic blood infused with penta-hybrid nanoparticles in an oviduct: Analytical and neural network modeling.

Computers in biology and medicine·2026
Same journal

sEEGnal: an automated EEG preprocessing pipeline evaluated against expert-driven preprocessing.

Computers in biology and medicine·2026
查看所有相关文章

相关实验视频

Updated: May 6, 2026

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
09:16

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

6.9K

基于全球关注的GNN与贝叶斯协作学习用于膜损伤识别.

Qiming He1, Shuang Ge2, Siqi Zeng3

  • 1Department of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China.

Computers in biology and medicine
|March 29, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新型图形神经网络 (GNN),用于识别膜病变的病. 这种先进的模型显著提高了诊断的准确性,为病理提供了一个强大的工具.

关键词:
贝叶斯协作学习是贝叶斯的协作学习.全球关注 全球关注淋巴细胞的损伤图表神经网络的神经网络病理学 病理学 病理学

更多相关视频

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

2.7K
Author Spotlight: Aiding Research in Kidney Biology by Labeling Glomeruli in Cleared Tissues
09:50

Author Spotlight: Aiding Research in Kidney Biology by Labeling Glomeruli in Cleared Tissues

Published on: February 9, 2024

1.3K

相关实验视频

Last Updated: May 6, 2026

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
09:16

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

6.9K
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

2.7K
Author Spotlight: Aiding Research in Kidney Biology by Labeling Glomeruli in Cleared Tissues
09:50

Author Spotlight: Aiding Research in Kidney Biology by Labeling Glomeruli in Cleared Tissues

Published on: February 9, 2024

1.3K

科学领域:

  • 计算病理学计算病理学
  • 脏病理学 脏病理学
  • 机器学习用于医学成像.

背景情况:

  • 淋巴细胞病变是病进展的关键指标.
  • 这些病变的病理诊断是明确的,但劳动密集型.
  • 当前的深度学习方法在病理图像中的复杂空间关系中扎.

研究的目的:

  • 开发一种先进的深度学习模型,用于准确识别质损伤.
  • 在病理学图像分析中克服欧几里德空间方法的局限性.
  • 为了提高特征提取和分类用于病诊断.

主要方法:

  • 提出了一个图形神经网络 (GNN) 与全球注意力聚合 (GAP) 进行语义特征提取.
  • 纳入贝叶斯协作学习 (BCL) 改进节点功能融合.
  • 实施软分类头来解决分类中的语义模糊性.

主要成果:

  • 在四个私人球细胞数据集上获得高F1分 (81.37%-98.68%).
  • 在球损伤识别任务中表现优于现有模型.
  • 在公众乳腺癌数据集 (85.61% F1评分) 上表现卓越.

结论:

  • 该GNN模型能够精确地识别淋巴细胞病变,并有助于诊断脏疾病.
  • 该框架可适应各种病理图像分类挑战.
  • 这种方法为病理和疾病诊断提供了一个强大的计算工具.