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相关概念视频

Discrete Fourier Transform01:15

Discrete Fourier Transform

228
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
228

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Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
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SELFNet:使用时空里埃特征网络否定剪切波弹性学.

Yanjun Xie1, Yi Huang1, John A Hossack1

  • 1Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.

Ultrasound in medicine & biology
|September 24, 2024
PubMed
概括

一个新的深度学习网络,SELFNet,有效地否定了超声波剪切波弹性图形数据,改善了组织硬度估计. 这种方法对癌症诊断等临床应用有希望.

科学领域:

  • 生物医学工程 生物医学工程
  • 医疗成像医学成像
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 超声剪波弹性图 (SWE) 通过分析剪波传播来估计组织硬度.
  • 在SWE中的有限收购可以将噪声引入移位或速度场.
  • 基于物理学的深度学习提供了一种新的方法,通过将控制方程余数最小化来近似物理领域.

研究的目的:

  • 引入剪切波弹性图形里埃特征网络 (SELFNet) 用于估计和否定粒子移位信号.
  • 在物理信息神经网络 (PINN) 框架内利用时空随机里埃特征.
  • 同时学习剪切模量映射和结合规则化治理方程.

主要方法:

  • 使用基于物理的深度学习框架开发了SELFNet.
  • 整合了时空随机的福里埃特征和稀疏的映射,以获得稳定性.
  • 对模仿组织的幽灵和ex vivo组织数据集的网络进行了评估.

主要成果:

  • 在不同硬度和大小的幻影病变中,SELFNet有效地平滑了噪音.
  • 实现了高斯过的优越性能,相对l2误差降低17%,RMSE降低45%.
  • 废弃研究证实,富里埃特征映射模块可以防止过度拟合.
关键词:
深度学习是一种深度学习.里埃特征映射的特征映射神经触点核心的神经触点核心颗粒的移位是粒子的移位基于物理学的神经网络.剪切波弹性图形学 剪切波弹性图形学超声波超声波是指超声波的使用.

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结论:

  • 通过有限的收购,SELFNet显示了改善SWE的巨大潜力.
  • 该方法的适用性在ex vivo组织研究中得到证实.
  • 成功的翻译可能会导致诊断癌症和肝脏疾病等疾病的临床应用.