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 Learning for Quality Control and Multimodal Integration of Shear Wave Elastography in Breast Lesion Characterization.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine·2026
Same author

Deep Learning for Point-of-Care Ultrasound M-Mode Analysis.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine·2026
Same author

Deep Learning for Automated Infant Hip Ultrasound: Toward Robust Generalization across Disease Spectrum and Devices.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine·2026
Same author

MedLP-HAFB-CLIP: Hierarchical Adaptive Large Model With Learnable Medical Prompts for Level II Ultrasound Standard Plane Identification.

Ultrasound in medicine & biology·2026
Same author

Deep Learning for Automated Quantification and Differential Diagnosis of Cutaneous Mastocytoma on Dynamic Ultrasound.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine·2026
Same author

Deep Learning for Ultrasound-Guided Prostate Biopsy: Toward Automated Targeting and Complication Prediction.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine·2026

相关实验视频

Updated: Jun 14, 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

通过生成对抗网络进行肺部图像细分.

Jiaxin Cai1, Hongfeng Zhu1, Siyu Liu2

  • 1School of Mathematics and Statistics, Xiamen University of Technology, Xiamen, China.

Frontiers in physiology
|September 2, 2024
PubMed
概括
此摘要是机器生成的。

生成性对抗性网络有效地分割肺部CT图像,以改善计算机辅助诊断. 这种新的方法提高了肺部疾病的检测和治疗计划.

关键词:
深度学习是一种深度学习.生成性的对抗性网络.图像处理是图像处理的过程.图像分割 图像细分 图像细分肺部图像分析 肺部图像分析机器学习是机器学习.

更多相关视频

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

459
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

379

相关实验视频

Last Updated: Jun 14, 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
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

459
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

379

科学领域:

  • 医学成像分析分析 医学成像分析
  • 医疗保健中的人工智能
  • 肺部病理学 肺部病理学

背景情况:

  • 精确的肺图像细分对于诊断和治疗肺部疾病至关重要.
  • 传统的细分方法经常面临复杂的肺部CT图像数据的挑战.

研究的目的:

  • 探索和评估一种使用生成对抗网络 (GAN) 的新型肺部CT图像细分方法.
  • 为了利用GANs的图像翻译能力,精确地对肺部图像进行细分.

主要方法:

  • 采用各种生成对抗网络架构用于图像细分.
  • 利用GAN的图像对图像翻译能力,将原始肺部图像转换为细分输出.
  • 在现实肺部图像数据集上测试了基于GAN的细分方法.

主要成果:

  • 拟议的基于生成对抗网络的细分方法在与最先进的技术相比显示出更高的性能.
  • 实验结果验证了GAN方法在实际肺部图像数据上的有效性.

结论:

  • 生成对抗网络为肺部图像细分提供了有效和强大的解决方案.
  • 这种方法在推进计算机辅助肺病诊断方面具有重大潜力.