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

Assessment of Diffusion and Perfusion01:17

Assessment of Diffusion and Perfusion

1.5K
Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
The Role of Diffusion in Respiration
Diffusion is the process by which molecules move from an area of higher concentration to an area of lower concentration. In the respiratory system, this...
1.5K

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

ERSegDiff: a diffusion-based model for edge reshaping in medical image segmentation.

Physics in medicine and biology·2024
Same author

Mechanisms involved in ceramide-induced cell cycle arrest in human hepatocarcinoma cells.

World journal of gastroenterology·2007
Same author

A population-based survey of women's traditional postpartum behaviours in Northern China.

Midwifery·2007
Same author

Colon carcinoma cells harboring PIK3CA mutations display resistance to growth factor deprivation induced apoptosis.

Molecular cancer therapeutics·2007
Same author

[Surgical treatment of 402 consecutive cases for hilar cholangiocarcinoma: Chinese single center experience].

Zhonghua wai ke za zhi [Chinese journal of surgery]·2007
Same author

Highly convergent route to cyclopeptide alkaloids: total synthesis of ziziphine N.

Organic letters·2007

相关实验视频

Updated: Jan 16, 2026

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

3.8K

CaliDiff:多级注释校准扩散概率模型向医学图像细分方向.

Junxia Wang1, Jing Wang2, Jun Ma3

  • 1School of Information Science and Engineering, Shandong Normal University, No. 1 Daxue Road, Changqing District, Jinan 250358, China; Department of Oncology, University of Helsinki, Fabianinkatu 33, Helsinki, Finland.

Medical image analysis
|September 26, 2025
PubMed
概括

CaliDiff是一种新的扩散概率模型,通过校准多个专家注释来完善医疗图像细分. 这种方法通过减少偏差和提高细分可靠性来提高诊断准确度和治疗规划.

关键词:
一致性规范化规范化扩散概率模型的扩散概率模型多级别注释校准多级别注释.光学磁盘和杯子的细分.

更多相关视频

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.4K
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.8K

相关实验视频

Last Updated: Jan 16, 2026

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

3.8K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.4K
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.8K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 医学图像细分对于诊断和治疗规划至关重要.
  • 目前的方法很难完全整合多样化的专家见解,并可能引入偏见.
  • 深度学习模型经常汇总专家标签,嵌入固有的偏见.

研究的目的:

  • 介绍CaliDiff,一个新的多级别注释校准扩散概率模型.
  • 利用多样化的专家知识和完善注释,以改善医疗图像细分.
  • 通过先进的注释校准,提高医学诊断的可靠性和客观性.

主要方法:

  • CaliDiff采用一个多阶段的过程,涉及共享参数反向扩散来实现偏差正常化.
  • 专业知识 一致一致 调整将注释差异最小化,并提高高信任地区的一致性.
  • 以委员会为基础的内生知识学习使用对抗软监督来生成伪基础真相,整合跨专家融合和隐式共识推断.

主要成果:

  • 在医疗图像中,CaliDiff显著改善了多级别注释的校准.
  • 该模型在医疗图像细分任务中实现了最先进的性能.
  • 实验评估表明诊断结果的可靠性和客观性得到了提高.

结论:

  • CaliDiff有效地解决了传统的多评分注释和深度学习聚合方法的局限性.
  • 拟议的模型增强了专家知识的利用,以实现更准确的医疗图像细分.
  • 在提高AI驱动的医学诊断的质量和可靠性方面,CaliDiff代表了重大进步.