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

Molecular mechanism of crisaborole combined with erythromycin against methicillin-resistant <i>Staphylococcus aureus in vivo</i> and <i>in vitro</i>.

Frontiers in microbiology·2025
Same author

Screening of cytokines-cytokine receptor-associated genes in childhood asthma based on bioinformatics.

Integrative biology : quantitative biosciences from nano to macro·2025
Same author

Subcritical water extraction improves the ability of Auricularia cornea var. Li. Polysaccharides to stabilize hydrogels and emulsion gels.

International journal of biological macromolecules·2025
Same author

SALT OVERLY SENSITIVE2 and AMMONIUM TRANSPORTER1;1 contribute to plant salt tolerance by maintaining ammonium uptake.

The Plant cell·2025
Same author

Transdermal microneedle-assisted ultrasound-enhanced CRISPRa system to enable sono-gene therapy for obesity.

Nature communications·2025
Same author

Homeostasis and metabolism of iron and other metal ions in neurodegenerative diseases.

Signal transduction and targeted therapy·2025

相关实验视频

Updated: Jul 5, 2025

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

2.8K

基于边缘精细化和高效的自我注意力的内镜外科仪器的轻量级细分网络.

Mengyu Zhou1,2, Xiaoxiang Han2, Zhoujin Liu1

  • 1School of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, P.R.China.

PeerJ. Computer science
|January 23, 2024
PubMed
概括

这项研究引入了一个新的轻量级模型,用于精确的手术仪器细分机器人手术. 该模型在显著减少参数的情况下实现了高精度,提高了外科医生的安全性和决策能力.

关键词:
有效的自我注意力.轻量级网络轻量级的网络.语义细分 语义细分是指语义细分.手术仪器 手术仪器

更多相关视频

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

405
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.5K

相关实验视频

Last Updated: Jul 5, 2025

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

2.8K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

405
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.5K

科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 医疗成像医学成像
  • 计算机视觉 计算机视觉

背景情况:

  • 手术仪器细分对于机器人辅助手术至关重要.
  • 现有的模型在精确的边缘和高参数数量方面扎,限制了部署.

研究的目的:

  • 开发一个轻量级的语义细分模型,用于准确的手术仪器细分.
  • 为了改善边缘精细化和减少模型复杂性,以便在实际应用中.

主要方法:

  • 使用轻量级密集连接网络,以高效地提取特征.
  • 采用了具有特征金字塔和交叉自我注意力的解码器,以实现多尺度集成和边缘精度.
  • 开发了一个私人数据集,并使用公共数据集 (Kvasir-instrument,Endovis2017) 进行培训和验证.

主要成果:

  • 在一个仅有466K个参数的私人数据集上,实现了97.11%的欧盟平均交叉点 (mIoU).
  • 在公开数据集上获得了93.24%和95.83%的优秀mIoU分数.
  • 与最先进的模型相比,在较低的参数下表现出卓越的性能.

结论:

  • 拟议的轻量级模型显著提高了手术仪器细分精度和边缘定义.
  • 该模型的效率和高性能为手术机器人的先进研究提供了基础.