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

Reinforcement Learning for Unsupervised Domain Adaptation in Spatio-Temporal Echocardiography Segmentation.

IEEE transactions on medical imaging·2026
Same author

Prompt Learning With Bounding Box Constraints for Medical Image Segmentation.

IEEE transactions on bio-medical engineering·2025
Same author

Harmonizing flows: Leveraging normalizing flows for unsupervised and source-free MRI harmonization.

Medical image analysis·2025
Same author

SARC-UNet: A coronary artery segmentation method based on spatial attention and residual convolution.

Computer methods and programs in biomedicine·2024
Same author

Boundary-aware information maximization for self-supervised medical image segmentation.

Medical image analysis·2024
Same author

What matters in reinforcement learning for tractography.

Medical image analysis·2024
Same journal

AdaWGAN: Data Augmentation for Few-Shot HD-sEMG Gesture Recognition Using Single-Trial Data.

IEEE journal of biomedical and health informatics·2026
Same journal

NeuroBooster: a domain-informed self-supervised learning paradigm tailored for brain MRI analysis.

IEEE journal of biomedical and health informatics·2026
Same journal

Graph Convolutional Neural Network based Depression Detection using Brain Functional Connectivity Measures.

IEEE journal of biomedical and health informatics·2026
Same journal

Improving Multi-Sensor Non-Invasive Glucose Detection through AI: A Domain Generalization Approach.

IEEE journal of biomedical and health informatics·2026
Same journal

Unmixing the Neck: Accurate Jugular Venous Pulse Detection From Wearable PPG.

IEEE journal of biomedical and health informatics·2026
Same journal

AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders.

IEEE journal of biomedical and health informatics·2026
查看所有相关文章

相关实验视频

Updated: Sep 10, 2025

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.3K

M $^{2}$ SegMamba:基于Mamba的不完整的多模式学习以少数样本进行脑瘤细分

Xinyue Zhang, Ali Bahri, Christian Desrosiers

    IEEE journal of biomedical and health informatics
    |August 20, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究介绍了M2SegMamba,一个使用Mamba和蒙面自编码网络进行脑瘤细分的新框架. 它有效处理不完整的多模式MRI数据和小样本大小,提高分段精度.

    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

    48.4K
    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

    2.8K

    相关实验视频

    Last Updated: Sep 10, 2025

    Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
    09:53

    Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

    Published on: August 16, 2020

    7.3K
    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

    48.4K
    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

    2.8K

    科学领域:

    • 医学成像
    • 人工智能
    • 计算生物学

    背景情况:

    • 精确的脑瘤细分对于临床诊断和治疗至关重要.
    • 多模磁共振成像 (MRI) 提供了丰富,互补的数据,但面临着不完整的模式和小样本大小的挑战.
    • 现有的多模式细分方法面临着数据稀缺和缺失的成像序列问题.

    研究的目的:

    • 开发一个强大的框架 (M2SegMamba) 用多模式MRI进行脑瘤细分,特别解决不完整的数据和小样本的挑战.
    • 利用Mamba和蒙面自编码网络进行监督和自我监督的学习.
    • 增强跨模式和跨模式图像特征的交互和整合.

    主要方法:

    • 设计了M2SegMamba框架,整合了Mamba和蒙面自动编码器网络.
    • 开发了一种针对多模式脑瘤的专用掩护策略,
    • 通过使用Mamba实现了增强模式间和模式间功能交互的多通道方法.
    • 将TSmamba纳入跳转连接以实现高效的多式联网功能集成.
    • 在编码器和解码器中使用辅助调节器来提高对不完整模式的稳定性.

    主要成果:

    • 与最先进的方法相比,M2SegMamba在脑瘤细分任务中表现出更好的表现.
    • 这一框架即使在2018年和2020年BraTS数据集中缺少模式,也实现了高准确性.
    • 结果表明处理不完整的多模式MRI数据的细分显著改善.

    结论:

    • M2SegMamba为脑瘤细分提供了强大而有效的解决方案,特别是在具有不完整多式数据和有限样本的具有挑战性的临床场景中.
    • 拟议的框架通过提高细分精度和可靠性来推动医疗图像分析领域的发展.
    • 这项研究强调了基于Mamba的架构在复杂的医学成像任务中的潜力.