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

RT-DETR-FCES: a lightweight ship detection algorithm from remote sensing perspective.

Scientific reports·2026
Same author

Community sport participation and mental wellbeing among women of reproductive age in China: A quantitative study.

African journal of reproductive health·2026
Same author

Linalool targets NR3C2 to inhibit NF-κB-mediated gastric cancer progression.

Cell division·2026
Same author

[(bpy)Cu(CF<sub>3</sub>)<sub>3</sub>]-Mediated Trifluoromethylation of Terminal Alkynes under Mild Conditions.

The Journal of organic chemistry·2026
Same author

Investigation of Flow Boiling Heat Transfer Performance of Grooved Metal Foam (Ni, Cu) Evaporators.

Micromachines·2026
Same author

Implantable and Stretchable Magnetoelectric Sensor for Motion-Assisted Wireless Signaling Using Biocompatible Piezoelectric Elastomer/Cobalt Ferrite Composites.

ACS applied materials & interfaces·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

Generalised Medical Phrase Grounding.

IEEE transactions on medical imaging·2026
Same journal

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

A Neural-Analytical Fusion Scatter Correction Method for Multi-Source CT Using Equivalent High-Order Scatter.

IEEE transactions on medical imaging·2026
查看所有相关文章

相关实验视频

Updated: Jul 11, 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

2.8K

自主监督深度无重建使用规范化通过denoising.

Peizhou Huang, Chaoyi Zhang, Xiaoliang Zhang

    IEEE transactions on medical imaging
    |November 14, 2023
    PubMed
    概括
    此摘要是机器生成的。

    这项研究介绍了DURED-Net,这是一种用于磁共振成像 (MRI) 重建的新型自主监督深度学习方法. 与现有的Noise2Noise方法相比,它显著减少了对标记训练数据的需求,同时提高了图像质量.

    更多相关视频

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    564
    Deep Learning-Based Segmentation of Cryo-Electron Tomograms
    10:25

    Deep Learning-Based Segmentation of Cryo-Electron Tomograms

    Published on: November 11, 2022

    8.9K

    相关实验视频

    Last Updated: Jul 11, 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

    2.8K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    564
    Deep Learning-Based Segmentation of Cryo-Electron Tomograms
    10:25

    Deep Learning-Based Segmentation of Cryo-Electron Tomograms

    Published on: November 11, 2022

    8.9K

    科学领域:

    • 医疗成像医学成像
    • 深度学习 (Deep Learning) 是一种深度学习.
    • 图像重建 图像的重建

    背景情况:

    • 深度学习在计算机视觉方面表现出色,并且越来越多地应用于MRI重建.
    • 将深度学习与基于模型的优化集成提供了优势,但需要大量的标记数据.
    • 在许多应用中,数据稀缺是高质量的MRI重建的一个重大挑战.

    研究的目的:

    • 开发一种新的,可解释的MRI重建自主监督学习方法.
    • 减少对大型标记数据集的依赖,以实现高质量的MR图像重建.
    • 通过结合成像物理先验来提高MRI重建中的Noise2Noise方法的性能.

    主要方法:

    • 拟议的DURED-Net,将自主监督的清除网络与插件运行方法相结合.
    • 通过Denoising (RED) 利用规范化来利用MRI重建的Denoising网络.
    • 从成像物理中获得的明确先验集成到重建过程中.

    主要成果:

    • DURED-Net 通过减少培训数据的数量实现了高质量的重建.
    • 该方法在MRI重建中与最先进的Noise2Noise方法相比显示出更高的性能.
    • 图像物理先验的整合提高了基于深度学习的重建的有效性.

    结论:

    • DURED-Net为可解释的自我监督的MRI重建提供了一个有效的解决方案.
    • 该方法解决了MRI应用中有限的标记数据的挑战.
    • 这种方法通过提高重建效率和质量来推进医学成像中的深度学习领域.