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

Early Prostate-Specific Antigen Dynamics as Predictors of Treatment Response and Survival Outcomes in Patients with Castration-Resistant Prostate Cancer and Bone Metastases Undergoing Radium-223 Therapy.

The world journal of men's health·2026
Same author

Revisiting p53 Immunohistochemical Staining and Its Prognostic Implications in Advanced EGFR-Mutated Lung Adenocarcinoma.

Cancers·2025
Same author

Prevalence and clinical impact of JAK2-CHIP: Association with Parkinsonism and hematologic changes in a population cohort.

Journal of the Formosan Medical Association = Taiwan yi zhi·2025
Same author

Design and Validation of a Deep Learning Model for Renal Stone Detection and Segmentation on Kidney-Ureter-Bladder Images.

Bioengineering (Basel, Switzerland)·2023
Same author

Lightweight Authentication Mechanism for Industrial IoT Environment Combining Elliptic Curve Cryptography and Trusted Token.

Sensors (Basel, Switzerland)·2023
Same author

Mutation-Driven S100A8 Overexpression Confers Aberrant Phenotypes in Type 1 <i>CALR</i>-Mutated MPN.

International journal of molecular sciences·2023

相关实验视频

Updated: Jul 25, 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

视网膜血管图像细分使用改进的基于残留模块的UNet.

Ko-Wei Huang1, Yao-Ren Yang1, Zih-Hao Huang1

  • 1Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan.

Bioengineering (Basel, Switzerland)
|June 28, 2023
PubMed
概括

这项研究引入了改进的U-Net深度学习模型,用于在医学图像中准确的视网膜血管细分. 人工智能模型提高了诊断效率,并优于对基准数据集的现有方法.

关键词:
深度学习是一种深度学习.医学图像医学图像神经网络的神经网络的神经网络视网膜血管细分器的细分

更多相关视频

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
Efficient and Consistent Generation of Retinal Pigment Epithelium/Choroid Flatmounts from Human Eyes for Histological Analysis
07:59

Efficient and Consistent Generation of Retinal Pigment Epithelium/Choroid Flatmounts from Human Eyes for Histological Analysis

Published on: October 28, 2022

2.8K

相关实验视频

Last Updated: Jul 25, 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
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
Efficient and Consistent Generation of Retinal Pigment Epithelium/Choroid Flatmounts from Human Eyes for Histological Analysis
07:59

Efficient and Consistent Generation of Retinal Pigment Epithelium/Choroid Flatmounts from Human Eyes for Histological Analysis

Published on: October 28, 2022

2.8K

科学领域:

  • 眼科医生 眼科 眼科
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 临床诊断和医学成像分析的深度学习正在迅速发展.
  • 传统的医学图像评估依赖于个体临床医生的专业知识,这可能是耗时和主观的.
  • 人工智能 (AI) 为高效的医疗信息评估提供了自动化分析和诊断援助的趋势.

研究的目的:

  • 为准确的视网膜血管细分提出一种新的机器学习架构.
  • 增强特征提取和结合多尺度信息,以提高细分性能.

主要方法:

  • 开发一个改进的U-Net神经网络模型,其中包含一个残余模块,用于有效的特征提取.
  • 实现全尺度跳过连接,以整合不同尺度的低级别细节与高级别特征.
  • 在基准数据集 (DRIVE和ROSE) 上进行实验评估,以评估细分精度.

主要成果:

  • 拟议的模型实现了视网膜血管图像的准确细分.
  • 该方法在DRIVE和ROSE数据集上表现出优异的性能,与现有的U-Net,ResUNet,U-Net3+,ResUNet++和CaraNet等模型相比.

结论:

  • 增强的U-Net架构提供了准确和高效的视网膜血管细分.
  • 这种人工智能驱动的方法支持临床医生在医学图像评估中,提高诊断效率.