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

Trends and disparities in valvular heart disease-related mortality among adults with diabetes mellitus in the United States: 1999-2024.

Journal of diabetes and metabolic disorders·2026
Same author

Predictive Role of Triglyceride-to-High-Density Lipoprotein-Cholesterol Ratio and Triglyceride-Glucose Index in Glycaemic Control among Type 2 Diabetes Mellitus Patients with Chronic Kidney Disease.

Journal of the College of Physicians and Surgeons--Pakistan : JCPSP·2026
Same author

Integration of temporal patterns, genotypic response, and environmental-determinants on aphid infestation in wheat.

Bulletin of entomological research·2026
Same author

Impact of Pre-Transplant Depth of Response on Outcomes in Patients With Multiple Myeloma: A Report on Behalf of Pakistan Blood and Marrow Transplant Group.

Asia-Pacific journal of clinical oncology·2026
Same author

Does Tumor Grade Have any Prognostic Significance in Chromophobe Renal Cell Carcinoma: A Clinicopathological Study.

Journal of cancer & allied specialties·2026
Same author

Chlorantraniliprole Resistance and Associated Fitness Costs in Fall Armyworm (<i>Spodoptera frugiperda</i>): Implications for Resistance Management.

Insects·2025

相关实验视频

Updated: Jun 25, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.0K

通过使用深度学习在数字病理学中进行可解释和通用化的线粒分裂检测.

Hasan Farooq1, Saira Saleem2, Iffat Aleem2

  • 1Computational Biology Research Lab, National University of Computer & Emerging Sciences, Islamabad, Pakistan.

Digital health
|May 23, 2024
PubMed
概括

这项研究引入了一种新的深度学习方法,用于在癌症诊断中准确检测线粒分裂. 该方法在数据集中表现出强大的性能和通用性,有助于数字病理学的采用.

关键词:
线粒分裂检测检测的检测深度学习是一种深度学习.数字病理学数字病理学可以概括的概括性.可解释的人工智能AI

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
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

相关实验视频

Last Updated: Jun 25, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.0K
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

2.7K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

科学领域:

  • 计算病理学计算病理学
  • 医学图像分析分析
  • 深度学习在瘤学中的应用.

背景情况:

  • 线粒体活动指数对于癌症预后至关重要.
  • 由于微观核,部分标记和类不平衡,准确检测线粒分裂是具有挑战性的.

研究的目的:

  • 为了应对当前线粒分裂检测管道中的挑战.
  • 提出一种新的深度学习方法,用于准确的线粒细胞核预测.

主要方法:

  • 利用MiDoG'22数据集进行培训,验证和测试.
  • 应用深度学习,其灵感来源于最近的研究和广泛的数据集分析.
  • 在TUPAC'16数据集和实时临床案例上验证了方法.

主要成果:

  • 在MiDoG'22数据集上获得0.87的F1得分.
  • 在TUPAC'16数据集上获得了0.83的F1得分.
  • 证明了有前途的定量和质量结果.

结论:

  • 提出的方法是准确的,可以概括和解释的.
  • 它显示了加速在临床环境中采用计算机辅助数字病理学的潜力.