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

Optimizing Chromatographic Separations01:15

Optimizing Chromatographic Separations

Optimizing chromatographic separations is crucial for obtaining clean separations in a minimum amount of time. Optimization is required for several factors, including kinetic effects related to band broadening, plate height, capacity factor, and separation factor.
Band broadening refers to spreading solute bands as they travel through the column. This broadening can impact resolution. Plate height (H) represents the length required for one theoretical plate. A lower plate height corresponds to...
High-Performance Liquid Chromatography: Introduction01:11

High-Performance Liquid Chromatography: Introduction

High-performance liquid chromatography(HPLC), formerly referred to as High-pressure liquid chromatography, is a powerful technique used to separate, identify, and quantify components in complex mixtures. The term "high pressure" refers to using high pressure to push the liquid mobile phase through the tightly packed columns.
In HPLC, two phases play a critical role in the separation process:
High-Performance Liquid Chromatography: Elution Process01:05

High-Performance Liquid Chromatography: Elution Process

In High-Performance Liquid Chromatography (HPLC), the elution process is critical to the separation of analytes and the quality of chromatographic results. Elution describes how compounds move through the column and separate based on their interactions with the mobile and stationary phases. This process determines the resolution, peak shape, and retention times in the chromatogram, which are essential for identifying and quantifying components in complex mixtures. Understanding the elution...
Special Staining Techniques01:13

Special Staining Techniques

Specialized staining techniques play a vital role in microbiology by enabling the visualization of specific bacterial structures that remain undetectable with standard microscopy methods. These techniques not only enhance the structural visualization of bacterial cells but also provide critical insights into their pathogenicity and classification. Additionally, they support diagnostic and research endeavors in microbiology by identifying key bacterial features.Capsule Staining for Virulence...

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

Clinical Manifestations.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Developing Topics.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Basic Science and Pathogenesis.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Cerebral small vessel disease lesion segmentation methods: A systematic review.

Cerebral circulation - cognition and behavior·2025
Same author

Signature White Matter Hyperintensity Locations Associated With Vascular Risk Factors Derived From 15 653 Individuals.

Stroke·2025
Same journal

Learning under constraints: a theoretical framework for comparing resource-constrained learning in biological and artificial systems.

Frontiers in computational neuroscience·2026
Same journal

MsGCN: a multi-stream graph convolutional network for multiband PLV graph fusion in EEG-based biometric identification.

Frontiers in computational neuroscience·2026
Same journal

AI-driven neuroanalytic modeling for mental health: multichannel CNN-based autism spectrum disorder detection via facial pattern analysis.

Frontiers in computational neuroscience·2026
Same journal

Modeling multiscale neural dynamics for EEG-based emotion recognition using an attentive wavelet-transformer framework.

Frontiers in computational neuroscience·2026
Same journal

New directions for complex systems in contemporary neuroscience: a morphodynamic and emergent function approach.

Frontiers in computational neuroscience·2026
Same journal

NMDA receptor kinetics drive distinct routes to chaotic firing in pyramidal neurons.

Frontiers in computational neuroscience·2026
查看所有相关文章

相关实验视频

Updated: Jun 7, 2026

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures
12:30

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

Published on: July 2, 2014

20.3K

多阶段的半监督学习增强了白质的高强度细分.

Kauê T N Duarte1,2, Abhijot S Sidhu3,4, Murilo C Barros5

  • 1Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.

Frontiers in computational neuroscience
|November 6, 2024
PubMed
概括
此摘要是机器生成的。

一种新的多阶段半监督学习 (M3SL) 方法有效地使用有限的注释数据对白质超强度 (WMHs) 进行细分. 这种方法提高了跨不同数据集和临床条件的概括性,优于传统和转移学习技术.

关键词:
阿尔茨海默氏症 (AD) 是一种疾病.这就是U-Net.卷积神经网络 (CNN) 是一种神经网络.多个阶段的学习学习.半监督学习 半监督学习白质超强度 (WMH) 是指白质的超强度.

更多相关视频

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

8.8K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

47.8K

相关实验视频

Last Updated: Jun 7, 2026

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures
12:30

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

Published on: July 2, 2014

20.3K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

8.8K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

47.8K

科学领域:

  • 医疗成像和人工智能的人工智能
  • 神经科学和神经学是神经科学和神经学.
  • 机器学习用于医疗保健

背景情况:

  • 白质超强度 (WMHs) 在老年人中很常见,并与痴呆和中风风险增加有关.
  • 手动对WMH进行细分是耗时和劳动密集的,阻碍了创建大型注释数据集.
  • 有限的注释数据对开发用于WMH细分的监督机器学习模型提出了重大挑战.

研究的目的:

  • 开发和评估一个多阶段的半监督学习 (M3SL) 方法,用于自动化白质超强度 (WMH) 细分.
  • 为了应对WMH细分中的有限注释数据的挑战,使用未注释和黄金标准注释数据的组合.
  • 改进WMH细分模型在不同MRI扫描仪供应商和临床人群 (认知正常,轻度认知障碍,阿尔茨海默病) 中的概括性.

主要方法:

  • 实施了多阶段的半监督学习 (M3SL) 框架,将未注释数据的传统处理方法 ("铜"和"银") 与一小部分"黄金"标准注释相结合.
  • 使用M3SL方法在U-Net架构中微调模型重量,用于WMH细分.
  • 通过使用来自三个供应商的多个扫描仪的数据和各种临床队列 (认知正常,MCI,AD) 的数据进行了模型的训练和验证.

主要成果:

  • 与传统和转移学习深度学习方法相比,M3SL方法在不同扫描仪供应商和临床阶段 (CN,MCI,AD) 展示了优越的概括性能.
  • 使用M3SL观察到WMH细分精度的显著改善,用F-测量,IoU和豪斯多夫距离 (p < 0.001) 来测量.
  • 该研究证实了在多阶段学习框架内使用自动化,非机器学习工具的实用性,以提高模型性能.

结论:

  • M3SL方法有效地克服了WMH分割的稀缺注释数据的局限性.
  • 这种方法提高了模型性能和概括性,为临床研究中大规模WMH分析提供了可行的解决方案.
  • 自动化工具在半监督学习框架中发挥着至关重要的作用,提高了医疗图像细分的效率和准确性.