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

Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.2K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.2K

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

Contrastive multimodal deep learning for survival prediction in grade 2/3 gliomas.

JNCI cancer spectrum·2026
Same author

Multimodal contrastive learning for non-invasive chondroid bone tumor classification and grading using radiographs.

BMC medical imaging·2026
Same author

Predicting targeted therapy resistance in non-small cell lung cancer using multimodal machine learning.

Journal of thoracic disease·2025
Same author

Transformer-based representation learning for robust gene expression modeling and cancer prognosis.

Scientific reports·2025
Same author

Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records.

The American journal of pathology·2025
Same author

'Ion-freeze' efficiency in perovskite solar cells: time scales for ion immobilization.

EES solar·2025

相关实验视频

Updated: Jul 23, 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

444

HistoPerm:一种基于 permutation 的视图生成方法,用于改进 histopathologic 特性表示学习.

Joseph DiPalma1, Lorenzo Torresani1, Saeed Hassanpour1,2,3

  • 1Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.

Journal of pathology informatics
|July 17, 2023
PubMed
概括
此摘要是机器生成的。

通过调整增强视图,HistoPerm增强了组织学图像分析,提高了数字病理学中的分类准确性,使用有限的标记数据. 这种方法可以提高各种深度学习模型的性能.

关键词:
数字病理学数字病理学联合嵌入架构的联合嵌入架构.代表性的学习学习.

更多相关视频

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

14.5K
DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma
09:52

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

Published on: June 6, 2025

277

相关实验视频

Last Updated: Jul 23, 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

444
Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
10:59

Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Published on: July 26, 2014

14.5K
DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma
09:52

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

Published on: June 6, 2025

277

科学领域:

  • 数字病理学数字病理学
  • 计算生物学 计算生物学
  • 机器学习 机器学习

背景情况:

  • 深度学习在数字病理学方面表现出色,但需要大量的标记数据.
  • 获取大型标记数据集用于组织学图像分析是资源密集的.

研究的目的:

  • 介绍HistoPerm,一种新的视图生成方法,用于组织学图像中的表示学习.
  • 用有限的标记数据增强使用联合嵌入架构的深度学习模型性能.

主要方法:

  • HistoPerm允许增强全幻灯片组织学图像补丁的视图.
  • 通过使用BYOL,SimCLR和VICReg.对乳病和细胞癌数据集进行评估.
  • 与完全监督的基线模型进行性能比较.

主要成果:

  • HistoPerm 持续提高了补丁和幻灯片级分类准确性,F1 评分和 AUC.
  • 在两个数据集的 BYOL,SimCLR 和 VICReg 模型中观察到显著的准确度提升.
  • 用HistoPerm增强的模型实现了与完全监督方法相比或优于完全监督方法的性能.

结论:

  • HistoPerm有效地改善了对具有有限标记数据的组织病理学特征的表示学习.
  • 该方法为增强数字病理学分析提供了有价值的工具.
  • 通过HistoPerm,可以实现高分类性能,而无需大量的手动标签.