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

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

4.6K
Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
4.6K

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

Comparative Effectiveness and Safety of Interventions for Atrial Fibrillation in Heart Failure: A Network Meta-Analysis of Randomized Trials.

Cardiovascular therapeutics·2026
Same author

Agmatine induces mitophagy via the PTS-I2R pathway to increase autophagic flux and attenuate sepsis-induced intestinal epithelial cell apoptosis.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]·2026
Same author

Iridium-Catalyzed Enantioselective C4-Alkylation of Indoles with α-Olefins and Styrenes.

Journal of the American Chemical Society·2026
Same author

mNGS-Identified <i>Mycobacterium porcinum</i> Infection in a Newly Diagnosed Person With HIV Presenting With Recurrent Suppurative Cervical Lymphadenitis.

Open forum infectious diseases·2026
Same author

Winter-associated downregulation of ovarian NR5A2 correlates with impaired follicle development in the striped hamster (Cricetulus barabensis).

Scientific reports·2026
Same author

Vapor-Phase Deposition of CsPbBr<sub>3</sub> Shells on Iodide-Rich Perovskite Cores in SiO<sub>2</sub> for Efficient and Robust Red Emission.

Small (Weinheim an der Bergstrasse, Germany)·2026

相关实验视频

Updated: May 31, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.0K

加强生物医学图像识别的合作-竞争代表性.

Junwei Jin1,2,3,4, Songbo Zhou3, Yanting Li5

  • 1The Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Ministry of Education, Zhengzhou, 450001, China.

Interdisciplinary sciences, computational life sciences
|January 22, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的强化协作-竞争性表示分类 (RCCRC) 方法,以改善生物医学图像分析中的人工智能. 通过更好地区分跨疾病的相似病理,RCCRC提高了诊断准确度.

关键词:
生物医学图像识别技术合作-竞争战略 合作-竞争战略规范化 规范化 规范化强化代表性 加强代表性基于代表性的分类.

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

2.6K
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

14.3K

相关实验视频

Last Updated: May 31, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.0K
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
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

14.3K

科学领域:

  • 生物医学图像分析
  • 医疗保健中的人工智能
  • 机器学习用于诊断.

背景情况:

  • 人工智能 (AI) 在生物医学图像分析方面表现有前途,但在区分跨疾病的类似病理和单一疾病内的变异方面扎.
  • 由于重叠的特征空间,现有的AI方法在准确分类复杂的生物医学图像方面存在局限性.

研究的目的:

  • 提出一种新的强化协作-竞争代表分类 (RCCRC) 方法,以克服人工智能驱动的生物医学图像分析的局限性.
  • 通过提高特征表示的辨别能力来提高AI的诊断效率.

主要方法:

  • 开发了RCCRC,一种将双重竞争约束纳入增强分类的目标函数的方法.
  • 集成的协作空间表示,以促进类之间的相似性和特定类子空间表示的独特性.
  • 利用统一的约束来探索重建空间中的全球和特定数据特征.

主要成果:

  • 在广泛的实验中,RCCRC方法在与最先进的分类算法相比显示出更高的性能.
  • 双重的竞争约束有效地提高了在生物医学图像中区分类似和不同的病理的能力.
  • 在各种生物医学图像数据库上的实验验验证了拟议的RCCRC方法的优势.

结论:

  • 拟议的RCCRC方法在基于AI的生物医学图像分类方面取得了重大进展.
  • RCCRC有效地解决了类似病理和疾病内部多样性所带来的挑战,从而提高了诊断准确性.
  • 这种方法有可能在医学诊断和图像分析中得到更广泛的应用.