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

The Structural Evolution of Recrystallized Asymmetric SiC Membranes for High-Performance Oily Wastewater Treatment.

Membranes·2026
Same author

Associations of MRI-derived Paraspinal IMAT and LMM with Cardiometabolic Risk Factors: Results from a German Cohort.

Radiology·2026
Same author

Current validation practice undermines surgical AI development.

ArXiv·2026
Same author

Decoding the surgical scene: A scoping review of scene graphs in surgery.

Medical image analysis·2026
Same author

Enhancing radiology workflows through collaborative AI-assisted chest X-ray reporting using large vision-language models: a proof-of-concept study.

Insights into imaging·2026
Same author

ConVibNet: needle detection during continuous insertion via frequency-inspired features.

International journal of computer assisted radiology and surgery·2026

相关实验视频

Updated: Feb 28, 2026

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

3.6K

无监督的域调整用于医疗图像细分,使用适应因子-扰动.

Hong Joo Lee1, Yuan Bi2, Sangmin Lee3

  • 1School of Computation, Information and Technology, Technical University of Munich, Munich, Germany; Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea.

Medical image analysis
|February 26, 2026
PubMed
概括

这项研究引入了一种新的无监督多目标域适应方法,用于医学AI. 它使用适应原-乱 (AP) 信号来改善不同数据集的模型性能,而无需共享私人患者数据.

关键词:
医疗图像细分 医疗图像细分多目标适应多目标适应无监督的域名适应

更多相关视频

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

845
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

3.9K

相关实验视频

Last Updated: Feb 28, 2026

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

3.6K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

845
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

3.9K

科学领域:

  • 医疗成像医学成像
  • 机器学习 机器学习
  • 计算机视觉 计算机视觉

背景情况:

  • 医疗人工智能的领域转移阻碍了预先训练的模型应用,原因是设备和患者的变化.
  • 现有的域调整方法通常需要单个目标调整或数据共享,这在医疗保健中引发了隐私问题.
  • 无监督域调整对于调整模型以适应不同的临床环境而不会损害患者隐私至关重要.

研究的目的:

  • 为医疗应用提出一种新的无监督的多目标域适应方法.
  • 为了应对领域转移的挑战,而不需要共享敏感患者数据.
  • 提高预训练模型在临床环境中的稳定性和通用性.

主要方法:

  • 引入了适应原 - 扰乱 (AP) 信号,以弥合源域和目标域的差距.
  • 将优化的AP注入到潜伏特征中,以促进模型适应.
  • 开发了一个光谱/几何一致性学习框架,用于无监督AP优化.

主要成果:

  • 拟议的方法有效地使预训练模型适应多个目标领域,而无需共享数据.
  • 适应原 - 乱 (AP) 在医疗图像细分任务中表现出显著的改进.
  • 频谱/几何一致性框架增强了对变化的模型稳定性.

结论:

  • 这种新的无监督多目标域适应方法对医疗AI有效.
  • 适应物 - 扰乱 (AP) 为医疗保健领域的域调整提供了一种保护隐私的解决方案.
  • 这种方法促进了AI在各种临床环境中的应用.