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相关概念视频

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
¹H NMR Signal Multiplicity: Splitting Patterns01:13

¹H NMR Signal Multiplicity: Splitting Patterns

When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...
Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a soft-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.To...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

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相关实验视频

Updated: May 10, 2026

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions
11:27

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions

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对可解释的多模态病理细分的光谱属性推理.

Lixin Zhang1, Qian Wang1, Zhao Chen1

  • 1School of Information and Intelligent Science, Donghua University, Shanghai, China.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
|January 17, 2026
PubMed
概括
此摘要是机器生成的。

Pisa-Net通过集成微观超光谱和RGB图像来提高计算病理的精确细分. 这种可解释的网络将预测与光谱证据联系起来,改善了数字病理学中的细胞和瘤细分.

关键词:
计算病理学计算病理学频率域是一个频率域.可解释的深度学习多模式细分化的多模式细分.推理 推理是一种推理.频谱属性学习的学习方法

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
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相关实验视频

Last Updated: May 10, 2026

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions
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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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科学领域:

  • 计算病理学计算病理学
  • 数字病理学数字病理学
  • 医疗图像分析 医学图像分析

背景情况:

  • 组织学实体的准确细分对于计算病理学和临床诊断至关重要.
  • 微观超光谱图像 (MHSI) 和RGB图像提供互补的病理信息.
  • 现有的多模式方法缺乏可解释性,并且无法充分利用MHSI中的光谱特征.

研究的目的:

  • 为MHSI和RGB图像开发一个可解释的多模式细分网络.
  • 解决现有方法在利用光谱特征方面的局限性,并提供明确的理由.
  • 为了提高细分性能和可解释性在计算病理学.

主要方法:

  • 提出了Pisa-Net,一种用于MHSI-RGB细分的病理解释性光谱属性学习网络.
  • 通过病理学家选择的特征学习的光谱签名来集成病理学知识.
  • 利用频域表示和适应性重量调整用于特征提取和融合.
  • 通过光谱证据引导的稀疏空间压缩,集成RGB和MHSI功能.

主要成果:

  • 在公共多模态病理学数据集上,Pisa-Net实现了对细胞,腺体和瘤的优异细分性能.
  • 通过明确将预测与病理学一致的光谱证据联系起来,证明了更好的解释性.
  • 展示了有效的多模式融合,符合病理学推理.

结论:

  • 在计算病理学中,Pisa-Net为可解释的多模式细分提供了一种新的方法.
  • 基于知识的光谱属性学习提高了细分的准确性和临床相关性.
  • 这种方法为细分预测提供了明确的,病理基础的光谱证据.