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

Deep dive into deep learning methods for cervical cancer detection and classification.

Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology·2025
Same author

Regulation of Notch signaling by multiple Ankyrin repeat containing protein Mask.

Cell communication and signaling : CCS·2025
Same author

Carboxymethyl Chitosan Capped Bimetallic Nanoparticles Entrapped in Theranostic Nanofibers: Antimicrobial Peptide Coating, In Vitro, In Vivo Characterization for MDR Microbial Infection and Photoacoustic/Optical Imaging.

ACS applied bio materials·2025
Same author

Notch and LIM-homeodomain protein Arrowhead regulate each other in a feedback mechanism to play a role in wing and neuronal development in <i>Drosophila</i>.

Open biology·2025
Same author

Loss of non-muscle myosin II Zipper leads to apoptosis-induced compensatory proliferation in Drosophila.

Biochimica et biophysica acta. Molecular cell research·2025
Same author

Corrigendum to "Nanofibers of N,N,N-trimethyl chitosan capped bimetallic nanoparticles: Preparation, characterization, wound dressing and in vivo treatment of MDR microbial infection and tracking by optical and photoacoustic imaging" [Int. J. Biol. Macromol. 263 (2024) 130154].

International journal of biological macromolecules·2024

相关实验视频

Updated: Jan 18, 2026

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
09:48

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

7.8K

优化宫癌的诊断,使用修改的HDFF进行精确的细胞分类.

Pooja Patre1, Dipti Verma2

  • 1Computer Science and Engineering, Vishwavidyalaya Engineering College Ambikapur, Ambikapur, Chhattisgarh, Ambikapur, India.

Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology
|September 8, 2025
PubMed
概括

一种新的修改后的层次深度特征融合 (HDFF) 方法显著提高了宫癌细胞分类的准确性. 这种自动化方法为早期检测提供了一个有希望的工具,并在宫癌查中改善了公共卫生结果.

关键词:
HDFF HDFF 的意思是高频高频.ML ML 在 ML宫癌:子宫癌是一种癌症.计算机辅助诊断系统 计算机辅助诊断系统

更多相关视频

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

812
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

811

相关实验视频

Last Updated: Jan 18, 2026

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
09:48

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

7.8K
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

812
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

811

科学领域:

  • 在瘤学瘤学.
  • 医疗成像医学成像
  • 计算机科学 计算机科学

背景情况:

  • 宫癌 (CC) 仍然是全球主要的健康问题,需要先进的诊断工具.
  • 传统的宫细胞分类方法是劳动密集型,容易出现错误,导致需要自动化.

研究的目的:

  • 引入和评估一种用于自动化宫细胞分类的新型修改层次深度特征融合 (HDFF) 方法.
  • 评估HDFF方法在SIPaKMeD和Herlev数据集上的性能,用于各种分类任务.

主要方法:

  • 该研究采用了修改后的层次深度特征融合 (HDFF) 方法.
  • 这种方法集成了层次的深度学习功能,以提高分类的准确性和稳定性.
  • HDFF方法结合了多个深度学习模型层的特征,以提高性能.

主要成果:

  • 修改后的HDFF方法在2类宫细胞分类中实现了98.88%的准确性,优于现有的模型.
  • 在多类任务中保持了高精度,回忆和F1分数,在3类问题中准确率为98.8%,在7类问题中准确率为98.5%.
  • 该方法与基于射频的层次分类 (98.43%在2类) 相比,表现优越.

结论:

  • 修改后的HDFF方法为宫癌查提供了一个高度准确和高效的工具.
  • 它在各种分类任务中的强表现表明了提高早期检测率的巨大潜力.
  • 以更大的数据集进行进一步开发,可以提高其在自动化宫癌检测系统中的实用性.