Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

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

MRI-based gross tumor volume delineation in high-grade glioma using a deep convolutional neural network.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026

Same author

Evaluating and Mitigating Carbon Dioxide Equivalent Emissions in Stroke Management: A Modeling Study.

Journal of the American Heart Association·2026

Same author

AI-augmented thyroid scintigraphy for robust classification of disease.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)·2026

Same author

A Heterogeneous Graph Neural Network Framework for Multi-Horizon Stroke Mortality Prediction.

medRxiv : the preprint server for health sciences·2026

Same author

Effectiveness of Physical Activity Interventions Using Wearables and Smartphone Applications for Individuals With Cardiovascular Diseases and Stroke: A Systematic Review and Meta-Analysis.

Journal of the American Heart Association·2026

Same author

Simultaneous partial volume correction and denoising of brain PET images, using transformers and transfer learning.

EJNMMI research·2026

Search research articles

相关实验视频

Updated: Jul 21, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

多机构PET/CT图像细分使用联合深度变压器学习.

Isaac Shiri¹, Behrooz Razeghi², Alireza Vafaei Sadr³

¹Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.

Computer methods and programs in biomedicine

|July 28, 2023

概括

此摘要是机器生成的。

联合学习 (FL) 能够为多机构PET/CT细分提供可靠的深度学习,克服数据共享的挑战. FL算法实现了与中央集中的头癌细分方法相提并论的性能.

关键词:

深度变压器深度变压器联合学习是联合学习.聚乙烯/聚乙烯/聚乙烯隐私隐私隐私隐私隐私隐私分段化分段化分段化分段化

更多相关视频

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

相关实验视频

Last Updated: Jul 21, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

针对PET/CT细分的深度学习模型需要大量,多样化的数据集.
法律,道德和隐私方面的担忧阻碍了多机构数据的共享.
联合学习 (FL) 提供了一个解决方案,可以在没有直接数据共享的情况下进行协作模式培训.

研究的目的:

开发和评估用于多机构PET/CT图像细分的联合学习 (FL) 框架.
评估各种FL算法的性能与集中式和单中心式方法相比.
解决医疗成像中可泛化和可信赖的深度学习方面的挑战.

主要方法:

使用了来自六个中心的328名头癌患者的数据集.
使用变压器网络进行双通道PET/CT图像细分.
七个FL算法 (ClQu,ZeQu,FedAvg,LoCo,RoAg,SeAg,GDP-AQuCl) 与集中和单一中心的基线进行了评估.

主要成果:

FL算法,特别是SeAg和GDP-AQuCl,实现了与集中方法 (迪斯系数0.80±0.11) 相似的性能.
所有FL方法都显示SUVmax和SUVmean的相对误差为<5%,表现优于单中心基线.
在大多数FL算法中没有观察到统计学上显著的性能差异.

结论:

开发的FL框架显示了PET/CT图像中HN瘤细分的有希望的性能.
尽管存在数据隐私限制,但FL能够创建可通用和可信的深度学习模型.
联合学习是多机构医学图像分析的可行方法.