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

Algebraic Connectivity Reveals Modulated High-Order Functional Networks in Alzheimer's Disease.

ArXiv·2026
Same author

Artificial Intelligence and Wearable Technologies for Upper Limb Neurorehabilitation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Microscopic Propagator Imaging with diffusion MRI.

Magnetic resonance imaging·2025
Same author

Wearable EEG-IMU based Framework for Investigating Neural Correlates of Motor-Cognitive Interaction in Multiple Sclerosis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Brain Connectivity Gradients Alterations in Discordant Cerebrospinal Fluid Profile for Alzheimer's Disease Biomarkers.

Human brain mapping·2025
Same author

Toward in-silico data assessment for passive BCIs: generating EEG rhythms with GANs.

Journal of neural engineering·2025

相关实验视频

Updated: Jul 9, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K

合并:一个多输入生物医学联合学习模式.

Bruno Casella1, Walter Riviera2, Marco Aldinucci1

  • 1Department of Computer Science, University of Turin, 10149 Turin, Italy.

Patterns (New York, N.Y.)
|November 30, 2023
PubMed
概括

这项研究引入了一种联合学习方法,将医疗图像和表格数据结合起来,以提高人工智能模型的准确性,同时保护患者的隐私. 这种方法提高了COVID-19和阿尔茨海默病等疾病的诊断能力.

关键词:
生物医学成像成像技术联邦分类联合分类.联合学习的联合学习混合数据深度学习的混合数据深度学习多输入分类多输入分类

更多相关视频

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

767
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.7K

相关实验视频

Last Updated: Jul 9, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

767
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.7K

科学领域:

  • 生物医学信息学是生物医学信息学.
  • 医疗保健中的人工智能
  • 机器学习用于医学成像.

背景情况:

  • 深度学习 (DL) 对于分析医疗图像至关重要,但往往忽视了有价值的表格患者数据.
  • 对于DL来说,大型数据集需要数据聚合,这带来了重大的隐私风险.
  • 联合学习 (FL) 通过在当地培训模型提供了一个保护隐私的解决方案.

研究的目的:

  • 开发和评估一个联合的多输入架构,整合医疗图像和表格数据.
  • 为了提高AI模型的性能和通用性,同时确保数据隐私.
  • 在现实世界生物医学应用中证明方法的有效性.

主要方法:

  • 实现了一个联合的多输入神经网络架构.
  • 在联合学习框架中整合了成像和表格患者数据.
  • 验证了COVID-19预后和阿尔茨海默病患者分层任务的方法.

主要成果:

  • 与单个输入模型相比,联合多输入模型实现了更高的准确性和F1得分.
  • 拟议的方法显示了比非联合方法更好的概括性.
  • 通过在各机构的本地培训模式来维护隐私.

结论:

  • 联合学习与多模式数据 (图像和表格) 相结合,显著提高了AI在生物医学任务中的性能.
  • 这种方法有效地解决了与大规模医疗数据分析相关的隐私问题.
  • 该方法在诊断和患者分层方面有望推进AI应用.