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相关概念视频

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

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Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
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Nuclear Localization Signals and Import01:46

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Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of  2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...
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Nuclear Overhauser Enhancement (NOE)01:07

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Irradiation of a spin-active nucleus causes an increase or decrease in the signal intensity of neighboring nuclei that are not necessarily chemically bonded or involved in J-coupling.  This phenomenon, called the Nuclear Overhauser Enhancement (NOE), results from through-space interactions between the nuclear spins. The NOE effect decreases with increasing internuclear distance and is generally not observed beyond 4 angstroms. In NOE, dipole-dipole interactions between neighboring...
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相关实验视频

Updated: Jul 8, 2025

Using Computer Vision Libraries to Streamline Nuclei Quantification
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基于位置的 anchor优化用于点监督密集核检测.

Jieru Yao1, Longfei Han2, Guangyu Guo1

  • 1Brain and Artificial Intelligence Lab, School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, 710072, China.

Neural networks : the official journal of the International Neural Network Society
|December 13, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的点监督方法,用于检测组织病理图像中的密集核,大大减少了癌症诊断中需要广泛的手动注释的需要. 该框架实现了高精度,在具有挑战性的密集核场景中接近完全监督的性能.

关键词:
癌症组织病理学图像 癌症组织病理学图像密集核检测检测 密集核检测基于形态学的伪标签在点监督学习学习.基于位置的杆优化

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科学领域:

  • 数字病理学数字病理学
  • 计算生物学是一种计算生物学.
  • 医疗图像分析 医学图像分析

背景情况:

  • 核的检测对于计算机辅助的癌症诊断至关重要,但在完全监督的方法中需要广泛的手动注释.
  • 弱监督的学习提供了一个解决方案,以减少注释负担,但检测密集,拥挤的核仍然是困难的.
  • 现有的方法与复杂的核分布和多样化的外观作斗争.

研究的目的:

  • 开发一种新的点监督框架,用于在基因病理图像中准确检测密集核.
  • 减少对耗时和专家依赖的手册注释的依赖.
  • 在拥挤和多样化的细胞环境中提高核检测性能.

主要方法:

  • 一个点监督密集核检测框架,利用基于位置的基优化和基于形态的伪标签监督.
  • 使用基于形态的机制生成细胞级伪标签 (CPL).
  • 实施基于位置的质量估计 (PAE) 以完善拥挤地区的检测.
  • 引入适应性选器 (AAS) 以基于核的特征进行坚固的选.

主要成果:

  • 拟议的框架在MO和Lizard基准指标上表现优于最先进的方法.
  • 实现了95.1%的完全监督性能,特别是在密集核检测场景中.
  • 使用ResNet50和PVTv2骨干验证的有效性.

结论:

  • 这种新的点监督方法有效地解决了密集核检测的挑战,并减少了注释工作.
  • 该框架显示了在数字病理学中增强计算机辅助诊断的巨大潜力.
  • 开发的方法为分析复杂的遗传病理图像提供了切实可行的解决方案.