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

Inflammatory protein mediators linking gut microbiota to degenerative lumbar spine disorders: cross-disease genetic evidence.

Frontiers in immunology·2026
Same author

Subthalamic segmentations in relation to deep brain stimulation volumes in Parkinson's disease.

Acta neurochirurgica·2026
Same author

Deep learning-based femoral reconstruction from intraoperative point clouds for enhanced knee arthroplasty registration.

International journal of computer assisted radiology and surgery·2026
Same author

Electron Irradiation Effects on N and Fe<sup>3+</sup> Doped Carbon Dots and the Application in Radiotherapy of Lung Cancer.

International journal of nanomedicine·2026
Same author

Characterizing forearm skeletal muscle composition and function in breast cancer-related lymphedema using B-mode ultrasonography.

Clinical physiology and functional imaging·2026
Same author

Towards user-centered interactive medical image segmentation in VR with an assistive AI agent.

Virtual reality·2026
Same journal

Co-assistant networks by pathology foundation model and convolutional neural network for gigapixel whole slide image analysis.

Medical image analysis·2026
Same journal

MBAS2024: A large-scale benchmark for multi-class bi-atrial segmentation in multi-center contrast-enhanced MRIs.

Medical image analysis·2026
Same journal

Respiratory motion augmentation for personalized super-resolution (RMApSR) of 3D cine MR images in MRI-guided radiotherapy.

Medical image analysis·2026
Same journal

Biom3d, a modular framework to host and develop 3D segmentation methods.

Medical image analysis·2026
Same journal

Embracing intra-class heterogeneity for semi-supervised medical image segmentation: From diversity to precision.

Medical image analysis·2026
Same journal

Real-time patient-specific microwave ablation zone prediction via a unified bioheat solver and MRI-informed perturbation learning.

Medical image analysis·2026
查看所有相关文章

相关实验视频

Updated: Sep 12, 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

2.9K

MedCLIP-SAMv2:朝着通用文本驱动的医疗图像细分的方向

Taha Koleilat1, Hojat Asgariandehkordi1, Hassan Rivaz1

  • 1Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada.

Medical image analysis
|August 8, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了MedCLIP-SAMv2,这是使用CLIP和Segment-Anything-Model (SAM) 等基础模型进行医学图像细分的新框架. 它可以通过更少的标签数据实现准确的细分,用于各种医学成像任务.

关键词:
基金会模型 基金会模型基于文本的图像细分,以图像为驱动.视觉语言模型 视觉语言模型监管薄弱的细分化细分化 监管薄弱的细分化

更多相关视频

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures
12:30

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

Published on: July 2, 2014

20.5K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

493

相关实验视频

Last Updated: Sep 12, 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

2.9K
A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures
12:30

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

Published on: July 2, 2014

20.5K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

493

科学领域:

  • 医学图像分析 医学图像分析
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 医疗图像的准确细分对于诊断和治疗至关重要.
  • 当前的深度学习方法通常需要大量的标记数据,从而限制了效率和通用性.
  • 基础模型为数据高效和交互式细分提供了潜力.

研究的目的:

  • 开发一个新的框架,MedCLIP-SAMv2,用于数据效率高的医疗图像细分.
  • 整合CLIP和分段-任何模型 (SAM) 进行文本提示分段.
  • 在各种医学成像模式的零射击和弱监督环境中评估框架.

主要方法:

  • 微调的生物医学CLIP与脱的硬负噪声对比估计 (DHN-NCE) 损失.
  • 利用多模式信息瓶 (M2IB) 进行视觉提示生成.
  • 在零射击和弱监督的设置中,使用文本提示以与SAM进行细分.

主要成果:

  • MedCLIP-SAMv2在细分各种医学成像任务方面表现出高准确度.
  • 该框架在零射击和弱监管细分方案中都表现出有效性.
  • 对超声波,MRI,X射线和CT成像数据进行了验证.

结论:

  • MedCLIP-SAMv2为医疗图像细分提供了一个强大且数据效率高的解决方案.
  • 基础模型的整合在临床环境中推进了交互式和通用细分.
  • 拟议的框架显示了改善疾病诊断和治疗规划的希望.