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
Overview of the Skull01:08

Overview of the Skull

7.4K
The cranium (skull) is the skeletal structure of the head that supports the face and protects the brain. It is subdivided into the facial bones and the brain case, or cranial vault. The facial bones underlie the facial structures, form the nasal cavity, enclose the eyeballs, and support the teeth of the upper and lower jaws.
The cranial vault surrounds and protects the brain and houses the middle and inner ear structures. This cavity is bounded superiorly by the rounded top of the skull, which...
7.4K
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

Learning-based non-linear registration robust to MRI-sequence contrast.

Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition·2026
Same author

Longitudinal FreeSurfer with non-linear subject-specific template improves sensitivity to cortical thinning.

Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition·2026
Same author

Improved, rapid fetal-brain localization and orientation detection for auto-slice prescription.

Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition·2026
Same author

Fast, automated slice prescription of standard anatomical planes for fetal brain MRI.

Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition·2026
Same author

MindGrab: A Spectrally-Motivated Architecture for Accessible Deep Learning in Neuroimaging.

NeuroImage·2026
Same author

In-vivo MRI-based assessment of placental morphology in growth-restricted fetuses.

Placenta·2026
查看所有相关文章

相关实验视频

Updated: Jan 9, 2026

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

7.2K

SingleStrip:从一个单一的标签示例中学习头骨剥离.

Bella Specktor-Fadida1, Malte Hoffmann2,3,4

  • 1Department of Medical Imaging Sciences, University of Haifa, Israel.

Data engineering in medical imaging : third MICCAI Workshop, DEMI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 27, 2025, Proceedings. DEMI (Workshop) (3rd : 2025 : Taejon-si, Korea)
|December 8, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的方法,将域随机化和自我训练相结合,用于深度学习细分,显著减少对标记的大脑MRI数据的需求. 这种方法即使使用最小的标记示例,也可以实现高性能,从而加速医学图像分析.

关键词:
深度学习是一种深度学习.一次性学习是一次性学习.质量控制质量控制质量控制细分化 细分化的细分化自己训练的自我训练.综合数据 综合数据

更多相关视频

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.0K
Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization
05:35

Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

Published on: April 19, 2017

7.0K

相关实验视频

Last Updated: Jan 9, 2026

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

7.2K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.0K
Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization
05:35

Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

Published on: April 19, 2017

7.0K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 神经科学是一个神经科学.

背景情况:

  • 对于像脑MRI这样的体积图像的深度学习细分需要大量的标记数据,这是一个重要的瓶.
  • 现有的域随机化方法具有有限的解剖变异性,标记数据稀缺.
  • 半监督的自我训练利用未标记的数据来克服标签的稀缺性.

研究的目的:

  • 开发一种新的半监督深度学习策略,用于使用最小标记的大脑MRI数据进行3D头骨剥离.
  • 将域随机化与自我训练相结合,以提高细分性能.
  • 评估基于自动编码器的质量控制方法,用于伪标签的选择.

主要方法:

  • 域随机化被用来从有限的标签地图合成训练图像.
  • 一个卷积自编码器 (AE) 在单个标记的示例上进行训练,用于预测面具的质量评估.
  • 根据AE重建错误排名的伪标签被用于微调骨剥离网络.

主要成果:

  • 结合的方法使得有效的3D头骨剥离只需要一个标记的例子.
  • 在分布之外的数据上的表现接近于用更多标记数据训练的模型的表现.
  • 基于AE的质量控制与基于一致性的排名相比,与细分精度的相关性更强.

结论:

  • 结合域随机化和基于AE的质量控制,可以从极其有限的标记数据中实现有效的半监督细分.
  • 这一策略可以显著减少医学成像研究中的标签负担.
  • 该方法对新的解剖结构和新兴的成像技术有很大的前景.