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

GDF11 Regulates Hypoxia-induced Pulmonary Endothelial Cell Pyroptosis Through SOX2/NLRP3 Axis.

European journal of pharmacology·2026
Same author

The safety and efficacy of human umbilical cord mesenchymal stem cell for acute respiratory distress syndrome: an open-label and multicenter phase 1 clinical trial.

Frontiers in immunology·2026
Same author

SMC4 is a hypoxia-responsive driver of pulmonary arterial hypertension through an HIF-1α-HDAC6-p21 regulatory axis.

Respiratory research·2026
Same author

A Dual-branch Network with Cross-scale Feature Interaction and Alignment for Weakly Supervised Whole Slide Image Analysis.

IEEE journal of biomedical and health informatics·2026
Same author

Inhibition of the QPCT-PDIA4 axis rescues ΔF508 and N1303K CFTR in cystic fibrosis.

Nature communications·2026
Same author

Anionic or Mixed-Charge Copolypeptides with Potent Antibiofilm Activities.

Biomacromolecules·2026

相关实验视频

Updated: Jun 29, 2025

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
09:16

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

6.9K

MSMTSeg:通过生成性自我监督的超级学习框架进行脏组织学图像的多层多组织细分.

Xueyu Liu, Rui Wang, Yexin Lai

    IEEE journal of biomedical and health informatics
    |March 25, 2024
    PubMed
    概括
    此摘要是机器生成的。

    这项研究介绍了MSMTSeg,这是一个新的AI框架,用于在病理图像中对脏组织进行细分. 它在最小的数据中显著提高了准确性,帮助病理学家诊断慢性病.

    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

    395
    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
    12:06

    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

    Published on: March 3, 2023

    4.0K

    相关实验视频

    Last Updated: Jun 29, 2025

    Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
    09:16

    Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

    Published on: June 18, 2020

    6.9K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    395
    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
    12:06

    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

    Published on: March 3, 2023

    4.0K

    科学领域:

    • 数字病理学数字病理学
    • 人工智能在医学中的应用
    • 计算生物学 计算生物学

    背景情况:

    • 慢性病诊断依赖于手动评估多染色组织结构,这是耗时的.
    • 现有的AI细分方法需要大量的手动注释,并与多色彩变化作斗争.

    研究的目的:

    • 开发用于多染色脏组织学的自动细分框架.
    • 解决当前人工智能方法中手动注释和单点域专注的局限性.

    主要方法:

    • 引入MSMTSeg,一个生成的自我监督的元学习框架.
    • 集成的多色调转换模型用于风格翻译.
    • 利用自我监督和元学习来实现域不变特征表示.

    主要成果:

    • 实现了优越的细分性能 (mDSC 0.836,mIoU 0.718) 对于具有最小注释的多染色组织 (每染色一个样本).
    • 在不同的污点和组织中表现出强度.
    • 优于现有的先进细分,少数拍摄和域调整方法.

    结论:

    • MSMTSeg为多染色脏组织学细分提供了一种可行且具有成本效益的解决方案.
    • 这种跨领域的技术有助于病理学家在临床实践中.
    • 减少了数字病理学工作流程中手动注释的负担.