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

LUMEN: LONGITUDINAL MULTI-MODAL RADIOLOGY MODEL FOR PROGNOSIS AND DIAGNOSIS.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same author

A comprehensive survey of computer vision methods for spatial transcriptomics.

Briefings in bioinformatics·2026
Same author

Current validation practice undermines surgical AI development.

ArXiv·2026
Same author

In silico modelling of changes in spinal cord blood flow after endovascular aortic aneurysm repair.

Computer methods and programs in biomedicine·2026
Same author

Text-Driven Tumor Synthesis.

IEEE transactions on medical imaging·2026
Same author

Computer Vision Methods for Spatial Transcriptomics: A Survey.

bioRxiv : the preprint server for biology·2025
Same journal

AMD-Mamba: A Phenotype-Aware Multi-modal Framework for Robust AMD Prognosis.

Machine learning in medical imaging. MLMI (Workshop)·2026
Same journal

Pseudo-Rendering for Resolution and Topology-Invariant Cortical Parcellation.

Machine learning in medical imaging. MLMI (Workshop)·2025
Same journal

Probabilistic 3D Correspondence Prediction from Sparse Unsegmented Images.

Machine learning in medical imaging. MLMI (Workshop)·2024
Same journal

Robust Unsupervised Super-Resolution of Infant MRI via Dual-Modal Deep Image Prior.

Machine learning in medical imaging. MLMI (Workshop)·2024
Same journal

MoViT: Memorizing Vision Transformers for Medical Image Analysis.

Machine learning in medical imaging. MLMI (Workshop)·2024
Same journal

Class-Balanced Deep Learning with Adaptive Vector Scaling Loss for Dementia Stage Detection.

Machine learning in medical imaging. MLMI (Workshop)·2024
查看所有相关文章

相关实验视频

Updated: Jun 9, 2025

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.3K

保护隐私的联邦脑瘤细分 保护隐私的联邦脑瘤细分

Wenqi Li1, Fausto Milletarì1, Daguang Xu1

  • 1NVIDIA.

Machine learning in medical imaging. MLMI (Workshop)
|October 25, 2024
PubMed
概括
此摘要是机器生成的。

联合学习可以在私人医疗数据上训练人工智能模型. 不同的隐私技术可以保护患者信息,但可能会影响模型性能,显示隐私和脑瘤细分精度之间的权衡.

更多相关视频

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

47.8K
Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning
08:41

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Published on: July 14, 2020

8.4K

相关实验视频

Last Updated: Jun 9, 2025

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

2.3K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

47.8K
Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning
08:41

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Published on: July 14, 2020

8.4K

科学领域:

  • 医疗人工智能 医疗人工智能
  • 机器学习隐私 机器学习隐私
  • 神经成像分析分析 神经成像分析

背景情况:

  • 医疗数据隐私法规阻碍了用于人工智能模型培训的集中数据收集.
  • 联合学习 (FL) 通过在本地培训模型和分享更新,而不是原始数据来解决这一问题.
  • 然而,FL模型更新仍可能无意中泄露敏感患者信息.

研究的目的:

  • 调查在联合学习框架内应用差异隐私 (DP) 技术的可行性.
  • 评估DP在联邦模型培训期间保护患者数据的有效性.
  • 评估DP对人工智能模型用于脑瘤细分的性能的影响.

主要方法:

  • 实施联合学习系统用于脑瘤细分.
  • 将差异性隐私机制集成和评估到联合学习过程中.
  • 使用BraTS数据集进行性能评估,比较带有和没有DP的模型.

主要成果:

  • 不同的隐私可以应用于医疗数据的联合学习设置.
  • 实验结果表明,隐私保护水平和实现的模型性能之间存在明显的权衡.
  • 该研究成功实施并评估了保护隐私的联合学习的实际系统.

结论:

  • 差异隐私是一种可行的方法,可以在医疗应用的联合学习中增强患者数据保护.
  • 在在联合学习系统中实施DP时,平衡隐私保证和模型准确性至关重要.
  • 需要进一步的研究来优化这种对神经成像临床实用性的权衡.