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

SynTME: A tumor microenvironment-aware, pharmacology-inspired multi-stage framework for drug synergy prediction.

Computer methods and programs in biomedicine·2026
Same author

Enhanced nitrogen and phosphorus removal from mining-affected waters by micro-nano aeration coupled with microbial remediation.

Environmental technology·2026
Same author

Comparative analysis of four nutritional scores in predicting hospital stay duration for EICU Patients with acute pancreatitis.

Frontiers in nutrition·2026
Same author

A TaKNOX1-TaAPO1-Rht1 feedback regulatory module orchestrates spikelet number and yield potential in wheat.

Plant communications·2026
Same author

In Situ Construction of Superhydrophobic Photothermal Coatings Based on Metal-Polyphenol Coordination Complex for Anti-/De-Icing Applications.

Polymers·2026
Same author

Dynamics of H3K4me3 and H3K36me3 histone modifications in response to powdery mildew infection in common wheat.

BMC plant biology·2026

相关实验视频

Updated: Jan 12, 2026

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

MsM-DPM:用于医疗图像分割的多尺度Mamba扩散概率模型.

Huaqiang Su, Haijun Lei, Zaiyi Liu

    IEEE transactions on cybernetics
    |November 4, 2025
    PubMed
    概括

    这项研究引入了多尺度的Mamba扩散概率模型 (MsM-DPM) 以改善医疗图像细分,有效处理复杂的损伤结构并增强特征表示以获得更好的准确性.

    科学领域:

    • 医学图像分析 医学图像分析
    • 深度学习 (Deep Learning) 是一种深度学习.
    • 计算机视觉 计算机视觉

    背景情况:

    • 传统的扩散概率模型 (DPM) 与不规则的医学图像结构和病变背景相似性作斗争.
    • 准确的医学图像细分对于诊断和治疗规划至关重要.

    研究的目的:

    • 为增强医疗图像细分提出一个创新的多尺度Mamba DPM (MsM-DPM) 架构.
    • 为了提高病变的形状和尺寸变化的稳定性.
    • 为了更好地捕捉病变边界细节和语义差异.

    主要方法:

    • 开发了集成多尺度注意力融合模块 (MSAFM) 和多尺度拒绝UNet (Ms-DU) 的 MsM-DPM.
    • 包含一个多层轴特征模块 (MLAFM),用于自适应的全球上下文聚合.
    • 利用一个多层次的全球上下文 (MLGC) 模块来重建跳过连接,以及一个功能融合模块 (FFM) 来增强边界细节.

    主要成果:

    • MsM-DPM在六个不同的数据集 (LUNA16,ATM22,COVID-19,自收集,胰腺,BT-MSD) 中表现出卓越的表现.
    • 架构有效地编码语义差异,改善了病变的特征表示.
    • 在实验评估中超越了现有的医疗图像细分方法.

    更多相关视频

    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

    3.7K
    Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
    09:06

    Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

    Published on: June 9, 2018

    12.6K

    相关实验视频

    Last Updated: Jan 12, 2026

    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
    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

    3.7K
    Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
    09:06

    Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

    Published on: June 9, 2018

    12.6K

    结论:

    • 拟议的MSM-DPM在医疗图像细分的准确性和稳定性方面取得了重大进展.
    • 新型架构有效地解决了复杂的医学成像场景中传统DPM的局限性.
    • MsM-DPM在需要精确的病变细分的临床应用中表现有前途.