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相关概念视频

Quantitative Analysis01:12

Quantitative Analysis

Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the method...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Applications of Molecular Taxonomy01:20

Applications of Molecular Taxonomy

Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...

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CDKN2A as transcriptomic marker for muscle-invasive bladder cancer risk stratification and therapy decision-making.

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Digital pathology imaging and computer-aided diagnostics as a novel tool for standardization of evaluation of aganglionic megacolon (Hirschsprung disease) histopathology.

Cell and tissue research·2018

相关实验视频

Updated: Jul 7, 2026

Protocols for Analyzing the Role of Paneth Cells in Regenerating the Murine Intestine using Conditional Cre-lox Mouse Models
07:48

Protocols for Analyzing the Role of Paneth Cells in Regenerating the Murine Intestine using Conditional Cre-lox Mouse Models

Published on: November 21, 2015

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使用可解释机器学习对不同结肠密码分支模式的定量分析.

Daniel Firmbach1,2, Corinna Lang-Schwarz2,3,4, Carlos A Rubio5

  • 1Institute of Pathology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.

Inflammatory bowel diseases
|October 29, 2025
PubMed
概括

机器学习模型现在可以在炎症性肠病 (IBD) 中区分对称和不对称的分支模式. 这种分类有助于更好地了解IBD亚型并提高诊断准确性.

关键词:
生物标志物 生物标志物影像成像技术 影像成像技术病理学的病理学

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Quantifying Branching Density in Rat Mammary Gland Whole-mounts Using the Sholl Analysis Method
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Quantifying Branching Density in Rat Mammary Gland Whole-mounts Using the Sholl Analysis Method

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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

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相关实验视频

Last Updated: Jul 7, 2026

Protocols for Analyzing the Role of Paneth Cells in Regenerating the Murine Intestine using Conditional Cre-lox Mouse Models
07:48

Protocols for Analyzing the Role of Paneth Cells in Regenerating the Murine Intestine using Conditional Cre-lox Mouse Models

Published on: November 21, 2015

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Quantifying Branching Density in Rat Mammary Gland Whole-mounts Using the Sholl Analysis Method
11:02

Quantifying Branching Density in Rat Mammary Gland Whole-mounts Using the Sholl Analysis Method

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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

482

科学领域:

  • 胃肠病学 胃肠病学
  • 计算病理学计算病理学
  • 历史学 历史学 历史学

背景情况:

  • 结肠密室分支是炎症性肠病 (IBD) 的一个关键组织学特征.
  • 观察到的分支模式包括对称和不对称的模式,表明不同的粘膜反应.
  • 这些模式的分类对于定量分析和IBD亚型的表征至关重要.

研究的目的:

  • 开发和验证用于分类结肠密码分支模式的机器学习模型.
  • 为了比较机器学习模型与专家病理学家的性能.
  • 为了增强IBD的组织学特征.

主要方法:

  • 用手工制作的形态特征来描述密室分支.
  • 开发了一种机器学习模型,并在专家注释的数据上进行训练.
  • 一个多级别的调查评估了模型和病理学家之间的分级别协议.

主要成果:

  • 经典组合模型实现了0.80平衡精度,而深度学习模型实现了0.79.
  • 整体模型显示与高级病理学家的中度一致.
  • 该研究成功地分类了不同的密码分支模式.

结论:

  • 机器学习模型可以有效地区分对称和不对称的密码分支.
  • 使用手工制作的功能提供了可解释性,与黑盒模型不同.
  • 这种方法为IBD组织学分析提供了一个透明和可靠的工具.