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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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LSOR:纵向一致的自我组织表示学习学习.

Jiahong Ouyang1, Qingyu Zhao1, Ehsan Adeli1

  • 1Stanford University, Stanford CA 94305, USA.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|November 14, 2023
PubMed
概括
此摘要是机器生成的。

我们开发了一种稳定的自我监督方法,称为纵向一致的自我组织表示学习 (LSOR),用于使用纵向脑MRI解释深度学习模型,根据大脑年龄分层表示.

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科学领域:

  • 神经成像是一种神经成像.
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 机器学习 机器学习

背景情况:

  • 深度学习模型在纵向脑MRI分析中的可解释性至关重要.
  • 自组织地图 (SOM) 可视化了高维的潜空间,但在自我监督的环境中却难以保持稳定性和临床相关性.
  • 现有的SOM方法本质上无法捕获临床上重要的信息,例如大脑年龄.

研究的目的:

  • 引入一种新的自我监督的SOM方法,用于从纵向大脑MRI中生成可解释的高维表示.
  • 在不需要人口或认知数据的情况下,根据大脑年龄对这些表示进行分层.
  • 提高SOM在神经成像深度学习中的稳定性和临床相关性.

主要方法:

  • 拟议的纵向一致的自我组织表示学习 (LSOR),是一种自主监督的SOM方法,利用软集群来增强训练稳定性.
  • 开发了一种技术,将纵向MRI轨迹与SOM集群参考向量对齐,根据大脑年龄分层潜伏空间.
  • 将LSOR应用于阿尔茨海默病神经成像计划 (ADNI) 数据集.

主要成果:

  • 从纵向脑MRI中,LSOR成功地产生了一个稳定和可解释的潜空间,按大脑年龄分层.
  • 该方法在下游任务上表现出与最先进的方法相比或更高的准确性.
  • 在分类 (轻度认知障碍进展) 和回归 (ADAS-Cog分数预测) 中取得了高绩效.

结论:

  • 通过深度学习,LSOR为分析纵向大脑MRI数据提供了一个强大而可解释的框架.
  • 该方法有效地将大脑年龄分层集成到代表性学习过程中.
  • LSOR为推进神经成像研究和临床应用提供了一个有前途的工具,特别是在阿尔茨海默病中.