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

Introduction to Learning01:18

Introduction to Learning

903
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
903
Neural Circuits01:25

Neural Circuits

2.6K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.6K
Observational Learning01:12

Observational Learning

795
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
795

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

Inter-Shot Motion Correction of Segmented 3D-GRASE ASL Perfusion Imaging With Self-Navigation and CAIPI.

Magnetic resonance in medicine·2026
Same author

SelExNet: A Self-Supervised Physics-Informed Framework for Multi-Channel Joint RF and Gradient Waveform Optimization in 2D Spatially Selective Excitation.

Magnetic resonance in medicine·2026
Same author

Learned k-space Partitioning for Optimized Self-Supervised MRI Reconstruction.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Combined angiography and perfusion using radial imaging and arterial spin labeling with structural contrast.

Magnetic resonance in medicine·2025
Same author

Few-shot learning for highly accelerated 3D time-of-flight MRA reconstruction.

Magnetic resonance in medicine·2025
Same author

Multi-site feasibility and reproducibility study on UTE 3D phosphorous MRSI using novel rosette trajectory (PETALUTE).

Magnetic resonance in medicine·2025
Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
查看所有相关文章

相关实验视频

Updated: Jan 9, 2026

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

996

学习使用深度神经网络的B0闪光模型.

Fatemeh Ebrahiminia, Mark Chiew

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    深度神经网络 (DNN) 可以准确估计磁共振 (MR) 波动线圈系数,改善磁场均性. 这种AI方法提高了MRI成像质量,并减少了临床环境中的扫描时间.

    更多相关视频

    Decoding Natural Behavior from Neuroethological Embedding
    08:00

    Decoding Natural Behavior from Neuroethological Embedding

    Published on: October 3, 2025

    562
    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    2.2K

    相关实验视频

    Last Updated: Jan 9, 2026

    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

    996
    Decoding Natural Behavior from Neuroethological Embedding
    08:00

    Decoding Natural Behavior from Neuroethological Embedding

    Published on: October 3, 2025

    562
    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    2.2K

    科学领域:

    • 生物医学工程 生物医学工程
    • 医学成像物理 医学成像物理

    背景情况:

    • 磁共振 (MR) 成像依赖于强大,均的磁场,以获得最佳的数据质量和采集速度.
    • 由于组织易感性和硬件问题引起的磁场异质性,需要B0闪光来纠正磁场变化.

    研究的目的:

    • 开发和评估一个深度神经网络 (DNN) 模型,以估计最佳的B0光圈系数.
    • 评估DNN模型在各种条件下补偿磁场干扰的性能.

    主要方法:

    • 一个模拟数据集被用来训练一个DNN模型来预测闪光线圈系数.
    • DNN模型的设计是为了学习复杂的场扰动模式和闪现场的隐含表示.
    • 在理想和非理想的闪光条件下使用R平方 (R2) 度量来评估模型性能.

    主要成果:

    • 基于DNN的闪光模型实现了R2=0.941±0.005.5的高性能.
    • 该模型表明,它能够预测各种闪光体积面罩的近最佳系数.
    • 在理想和非理想的磁场场景中,DNN方法被证明是有效的.

    结论:

    • 深度神经网络提供了一种快速有效的方法来估计MRI成像中的B0闪光系数.
    • 这种人工智能驱动的方法有可能显著减少扫描时间,并改善临床MR应用中的图像质量.
    • 拟议的模型显示了直接在扫描仪上实时应用的希望.