Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

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

Using clinical and radiographic variables to predict intracranial aneurysm rupture status with machine learning.

Surgical neurology international·2025

Same author

A novel collaborative self-supervised learning method for radiomic data.

NeuroImage·2023

Same author

Effects of an Intermittent Fasting 5:2 Plus Program on Body Weight in Chinese Adults with Overweight or Obesity: A Pilot Study.

Nutrients·2022

Same author

Accessibility of essential anticancer medicines for children in the Sichuan Province of China.

Frontiers in public health·2022

Same author

The delivery of nanoparticles improves the pharmacokinetic properties of celecoxib to open a therapeutic window for oral administration of insoluble drugs.

Biomedical chromatography : BMC·2022

Same author

Elevated serum CA72-4 predicts gout flares during urate lowering therapy initiation: a prospective cohort study.

Rheumatology (Oxford, England)·2022

Same journal

Real-time EEG-based epileptic seizure prediction using artificial intelligence: A systematic review.

Artificial intelligence in medicine·2026

Same journal

R-peak detection and ECG data compression scheme based on empirical mode decomposition and wavelet transform.

Artificial intelligence in medicine·2026

Same journal

CastNet: A three-channel EEG-based deep learning model for cross-subject depression detection.

Artificial intelligence in medicine·2026

Same journal

State-of-the-art TinyML approaches for colorectal cancer detection: Current advances, challenges, and future directions.

Artificial intelligence in medicine·2026

Same journal

JRadiEvo: A Japanese radiology report generation model enhanced by evolutionary optimization of model merging.

Artificial intelligence in medicine·2026

Same journal

Causally-informed deep learning towards explainable and generalizable outcome prediction in critical care.

Artificial intelligence in medicine·2026

查看所有相关文章

Search research articles

相关实验视频

Updated: Jun 11, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

联合自我监督和监督对比学习多模式MRI数据:朝着预测异常神经发育的方向.

Zhiyuan Li¹, Hailong Li², Anca L Ralescu³

¹Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA.

Artificial intelligence in medicine

|October 6, 2024

概括

此摘要是机器生成的。

这项研究引入了一种新的深度学习方法,用于融合多模式磁共振成像 (MRI) 数据. 该方法通过有效地结合来自不同MRI类型的信息来增强异常神经发育的预测.

关键词:

深度多式调节学习 (Deep Multimodal Learning) 是一种多式调节学习方式.疾病的诊断疾病的诊断共同的对比学习学习.多模式核磁共振 (MRI) 是一种多模式核磁共振.

更多相关视频

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

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

Published on: June 30, 2020

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

相关实验视频

Last Updated: Jun 11, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

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

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

Published on: June 30, 2020

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

科学领域:

神经成像是一种神经成像.
人工智能的人工智能
医学诊断医学诊断医学诊断

背景情况:

多式磁共振成像 (MRI) 数据与深度学习的整合显示出疾病诊断的前景.
目前的方法难以有效地融合异质的多式联运特征,导致冗余和互补信息的丢失.
强大的特征表示对于充分利用多模式MRI的全部潜力至关重要.

研究的目的:

开发一种新的联合自我监督和监督对比学习方法,用于多模式MRI数据.
通过将异质特征投射到共享的共同空间中来学习强大的潜在特征表示.
为了改进分析,将不同MRI模式和主题的互补和相似信息合并在一起.

主要方法:

设计了一个联合的自我监督和监督的对比学习框架.
该方法将异构的多模体MRI特征投射到共享的潜空间中.
对其他深度多式联络学习方法进行了比较分析.

主要成果:

拟议的方法显著优于其他几种深度多式联络学习技术.
在两个独立的数据集上进行了实验,证明了卓越的性能.
这种方法在预测异常神经发育方面被证明是有效的.

结论:

这种新的对比学习方法为多模式MRI数据融合提供了一种优越的方法.
这种技术可以提高临床实践中计算机辅助诊断的准确性.
该方法有效地利用多式联络数据的力量,改善神经发育障碍的预测.