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

Seizures: Classification01:13

Seizures: Classification

2.5K
Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
2.5K

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

Bayesian Uncertainty-aware Deep Learning with noisy labels: Tackling annotation ambiguity in EEG seizure detection.

PloS one·2026
Same author

The evolving role of artificial intelligence in ophthalmology: basic science, translation, and clinical integration.

Current opinion in ophthalmology·2026
Same author

LLM-Powered Cross-Modal Alignment for Explainable Seizure Detection from EEG.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same author

BiSCoT: Behavior-Informed Subgroup-Consistent Connectome Template for Interpretable Brain Network Analysis.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same author

Learning Explainable Imaging-Genetics Associations Related to a Neurological Disorder.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same author

GAMing the Brain: Investigating the Cross-modal Relationships between Functional Connectivity and Structural Features using Generalized Additive Models.

Machine learning in clinical neuroimaging : 7th international workshop, MLCN 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. MLCN (Workshop) (7th : 2024 : Marrakesh, Morocco)·2026

相关实验视频

Updated: May 6, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.0K

一个深度学习框架,以表征噪音标签在发性区域的本地化使用功能连接.

Naresh Nandakumar1, David Hsu2, Raheel Ahmed3

  • 1Department of Electrical and Computer Engineering, Johns Hopkins University, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|October 28, 2024
PubMed
概括

这项研究引入了一种新的深度学习框架,可以准确地确定患者的发性区域 (EZ),即使有杂的术后成像数据. 该方法通过考虑地面真相标签中的不确定性来改善发作本地化.

关键词:
动态功能连接 动态功能连接是一种病.噪音标签 噪音标签半监督学习 半监督学习

更多相关视频

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

11.1K
Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.3K

相关实验视频

Last Updated: May 6, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.0K
A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

11.1K
Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.3K

科学领域:

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

背景情况:

  • 休息状态功能性MRI (rs-fMRI) 是在耐药焦点中定位发性区域 (EZ) 的关键工具.
  • 具有精确EZ标签的临床数据集是有限的,通常依赖于杂的切除区域数据作为基本真相.
  • 这种噪音的产生是因为切除区域通常超过实际的EZ组织边界.

研究的目的:

  • 使用rs-fMRI开发一种数学框架,用于在EZ本地化中描述和处理噪音标签.
  • 通过解决标签不确定性,提高患者EZ局部化的准确性.

主要方法:

  • 开发了一个多任务深度学习框架,同时预测EZ本地化和标签噪声的概率.
  • 该框架是基于来自人类结合体项目的模拟数据进行训练的.
  • 对模拟和真实世界的临床数据集进行了评估.

主要成果:

  • 与现有方法相比,拟议的框架显示出优越的EZ本地化性能.
  • 这种提高的准确性在模拟和临床数据集上都被观察到.
  • 该方法有效地识别了噪音标签,有助于更可靠的本地化预测.

结论:

  • 开发的数学框架和多任务深度学习方法有效地解决了EZ本地化中的噪音标签.
  • 这种方法为识别发性区域提供了显著的进步,这对于的手术规划至关重要.
  • 这些发现表明,在临床实践中,基于rs-fMRI的EZ局部化方法更强大,更准确.