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

Praxis-BGM: clustering of omics data using semi-supervised transfer learning for Gaussian mixture models via natural-gradient variational inference.

Bioinformatics (Oxford, England)·2026

Same author

A false discovery rate control method using a fully connected hidden Markov random field for neuroimaging data.

Medical image analysis·2026

Same author

Hypertension is related to a slower radiotracer removal from lateral ventricles.

bioRxiv : the preprint server for biology·2026

Same author

Advances in the drug treatment of Alzheimer's disease: pathophysiology and mechanisms of action.

BMJ (Clinical research ed.)·2026

Same author

Cross-sectional investigation of choroid plexus calcification, cardiovascular risk score, and APOEε4 Status in cognitively normal cohort.

Cerebral circulation - cognition and behavior·2026

Same author

Unleashing Diffusion and State Space Models for Medical Image Segmentation.

Journal of imaging informatics in medicine·2026

Same journal

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift.

Proceedings of machine learning research·2026

Same journal

Endo-SemiS: Towards Robust Semi-Supervised Image Segmentation for Endoscopic Video.

Proceedings of machine learning research·2026

Same journal

Perspective: Machine Learning for Health Should Consider Social Drivers of Health.

Proceedings of machine learning research·2026

Same journal

Classifying Phonotrauma Severity from Vocal Fold Images with Soft Ordinal Regression.

Proceedings of machine learning research·2026

Same journal

Does Domain-Specific Retrieval Augmented Generation Help LLMs Answer Consumer Health Questions?

Proceedings of machine learning research·2026

Same journal

Quantitative Convergence Analysis of Projected Stochastic Gradient Descent for Non-Convex Losses via the Goldstein Subdifferential.

Proceedings of machine learning research·2026

查看所有相关文章

Search research articles

相关实验视频

Updated: Jun 26, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

DeepFDR:一种基于深度学习的虚假发现率控制方法,用于神经成像数据.

Taehyo Kim¹, Hai Shu¹, Qiran Jia^1,2

¹Department of Biostatistics, School of Global Public Health, New York University.

Proceedings of machine learning research

|May 14, 2024

概括

此摘要是机器生成的。

通过使用深度学习来控制空间错误发现率 (FDR),DeepFDR增强了神经成像分析. 这种新的方法提高了基于voxel的测试的统计能力和效率,优于传统方法.

更多相关视频

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

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 26, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

科学领域:

神经成像数据分析的数据分析.
神经成像中的统计推断.
机器学习在医学成像中的应用.

背景情况:

基于voxel的多重测试是神经成像的标准,但由于忽视了空间依赖,传统方法缺乏力量.
现有的空间错误发现率 (FDR) 方法与复杂的大脑空间依赖性作斗争.
深度学习在图像细分方面表现出色,这是一个相关的任务,为改进的神经图像分析提供了潜力.

研究的目的:

引入DeepFDR,一种新的空间FDR控制方法,用于基于voxel的神经图像分析.
为了利用基于深度学习的无监督图像细分来增强FDR控制.
提高神经成像研究中的统计能力和计算效率.

主要方法:

开发了DeepFDR,将用于图像分割的无监督深度学习集成到空间FDR控制中.
进行了全面的模拟,以评估DeepFDR的性能.
应用DeepFDR对阿尔茨海默氏病的FDG-PET图像分析.

主要成果:

与现有方法相比,DeepFDR在模拟和真实世界的神经成像数据中都表现出了优越的性能.
该方法实现了有效的FDR控制,并显著降低了虚假不发现率.
DeepFDR表现出高计算效率,适合大规模的神经成像数据集.

结论:

在神经成像中,DeepFDR为基于voxel的多重测试提供了强大而高效的解决方案.
深度学习的整合改善了空间FDR控制,解决了传统和现有的空间方法的局限性.
DeepFDR是推动神经成像研究的一个有前途的工具,特别是在阿尔茨海默氏症等疾病分析中.