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

Effectiveness of Individualized Counselling Session (ICS) on Robotic Assisted Surgery (RAS) to reduce stress and anxiety among parents of children undergoing Robotic Assisted Surgery in SGPGIMS, Lucknow.

Journal of robotic surgery·2026
Same author

Enhanced skin cancer classification for minority classes using Conditional GAN pipeline and CNN-ViT ensemble.

Scientific reports·2026
Same author

Haematological trends and associated congenital anomalies in children with cleft lip and palate.

The Medical journal of Malaysia·2025
Same author

Brain tumour segmentation in fused MRI-PET images with permutate U-Net framework.

PloS one·2025
Same author

A hybrid recurrent neural network and optimization framework for intelligent mobile robot navigation in smart manufacturing.

Scientific reports·2025
Same author

Low-dose computed tomography image denoising using pixel level non-local self-similarity prior with non-local means for healthcare informatics.

Scientific reports·2025

相关实验视频

Updated: Jul 10, 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

展开深核估计注意力基于UNet的视网膜图像细分.

K Radha1, Karuna Yepuganti1, Saladi Saritha2

  • 1School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.

Scientific reports
|November 24, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了改进的注意力U-Net模型,用于精确的视网膜血管细分在 fundus 图像中,这对于早期糖尿病视网膜病变检测至关重要. 该方法提高了准确性和效率,有助于诊断眼睛疾病.

更多相关视频

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

559
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

406

相关实验视频

Last Updated: Jul 10, 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
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

559
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

406

科学领域:

  • 眼科医生 眼科 眼科
  • 医疗成像医学成像
  • 计算机视觉 计算机视觉

背景情况:

  • 糖尿病视网膜病变是视力丧失的主要原因,需要通过视网膜图像分析进行早期检测.
  • 在 fundus 图像中的自动视网膜血管细分对于查和诊断糖尿病视网膜病变至关重要.
  • 当前的方法在准确性和计算效率方面面临挑战.

研究的目的:

  • 开发一种精确且计算效率高的视网膜血管细分方法.
  • 为了提高视网膜图像自动化分析的准确性和可靠性,用于诊断眼睛疾病.
  • 为有限的培训数据场景增强语义细分模型.

主要方法:

  • 提出了Attention U-Net架构,其中包含了用于集中图像区域分析的注意力机制.
  • 整合了展开的深核估计 (UDKE) 方法以提高语义细分性能.
  • 在STARE,DRIVE和CHASE_DB数据集上进行了实验.

主要成果:

  • 拟议的方法在视网膜血管细分方面表现出强的表现.
  • 与现有的最先进的方法相比,取得了具有竞争力的结果.
  • 展示了更好的准确性,特别是在有限的培训数据下.

结论:

  • 增强的注意力U-Net与UDKE提供了一个有前途的方法,用于准确和高效的视网膜血管细分.
  • 这种技术可以显著帮助早期检测和治疗糖尿病视网膜病变和其他眼睛疾病.
  • 该方法在基准数据集上的有效性验证了其临床应用潜力.