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

Long-Term Results of Nail Correction with Distal Phalanx Osteotomy for Pincer Nail Deformity: A Retrospective Cohort Study.

Indian journal of dermatology·2026
Same author

Effect of isoginkgetin on targeting methylenetetrahydrofolate dehydrogenase 2 to induce ROS-mediated apoptosis in non-small cell lung cancer.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026
Same author

Inhibition of STAT3-mediated glycolysis by bruceine D suppresses non-small-cell lung cancer progression <i>in vitro</i> and <i>in vivo</i>.

Cancer biology & therapy·2026
Same author

BANF1 as a potential prognostic biomarker associated with tumor-intrinsic programs and a complex immune landscape in lung adenocarcinoma.

Discover oncology·2026
Same author

Deoxypodophyllotoxin inhibits lung adenocarcinoma growth through regulation of FOXO1 nuclear translocation.

Biochemical pharmacology·2026
Same author

Evaluation of the Residual Stress in ZrO<sub>2</sub> Coatings Deposited on Different Substrates Through Image Relative Method.

Materials (Basel, Switzerland)·2026

相关实验视频

Updated: Jun 24, 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

2.7K

STCS-Net:一个医疗图像细分网络,充分利用多层次信息.

Pengchong Ma1,2, Guanglei Wang1,2, Tong Li1,2

  • 1College of Electronic And Information Engineering, Hebei University, Hebei 071002, China.

Biomedical optics express
|June 10, 2024
PubMed
概括

本研究介绍了STCS-Net,这是一种用于医学图像细分的新型深度学习模型. 它的增强解码器和跳过连接显著提高了医学成像中的特征提取精度和效率.

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

388
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.7K

相关实验视频

Last Updated: Jun 24, 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

2.7K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

388
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.7K

科学领域:

  • 医学图像分析 医学图像分析
  • 医疗保健中的深度学习
  • 计算机视觉 计算机视觉

背景情况:

  • 深度学习显著推进了医疗图像细分.
  • 当前的研究经常优先考虑编码器优化.
  • 解码器对于完善图像细节和利用各种信息至关重要.

研究的目的:

  • 提出STCS-Net,一种新的医疗图像细分架构.
  • 为了提高特征提取精度和层间信息交互.
  • 为了提高医疗图像细分模型的性能.

主要方法:

  • 开发了STCS-Net,配备了一个专门的解码器,用于多级别的过和校正.
  • 在跳过连接中引入了一个信息增强模块.
  • 对ISIC2016,ISIC2018和肺部数据集进行了全面评估.

主要成果:

  • 在多个医学成像数据集中,STCS-Net表现出卓越的性能.
  • 与现有方法相比,实现了卓越的准确性和参数效率.
  • 废弃性研究证实了拟议的解码器和跳过连接模块的有效性.

结论:

  • STCS-Net为医疗图像细分提供了一种新且有效的方法.
  • 拟议的架构增强了特征提取和层间通信.
  • 这项研究为未来的医学图像处理和分析提供了宝贵的见解.