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相关概念视频

Classification of Epithelial Tissues: Overview01:22

Classification of Epithelial Tissues: Overview

Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
Based on the number of cell layers,...
Classification of Epithelial Tissues: Stratified Epithelium01:29

Classification of Epithelial Tissues: Stratified Epithelium

Stratified epithelium consists of several stacked layers of cells. They provide the durability to withstand constant physical and chemical attacks. Stratified epithelium is named after the shape of the most apical layer of cells. Stratified squamous epithelium is the most common type found in the human body. In this tissue, the apical cells are squamous, whereas the basal layer contains either columnar or cuboidal cells. The basal cells divide to form new daughter cells, which gradually become...
Classification of Connective Tissues01:30

Classification of Connective Tissues

The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense.

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Updated: May 10, 2026

Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
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从多个解读器使用链式深度学习方法对乳腺癌组织进行分类.

Andres Felipe Valencia-Duque, David Augusto Cardenas-Pena, Julian Gil-Gonzalez

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    概括
    此摘要是机器生成的。

    乳腺癌分类的计算机辅助诊断 (CAD) 面临着有限的标记数据的挑战. 使用泛化交叉损失 (GCEDL) 的多注释器方法证明了稳定性,接近专家的性能.

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    Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
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    科学领域:

    • 医疗成像医学成像
    • 计算病理学计算病理学
    • 人工智能在医学中的应用

    背景情况:

    • 乳腺癌的诊断依赖于活检,这可能是主观的,并导致专家间的分歧.
    • 计算机辅助诊断 (CAD) 系统旨在提高效率并减少诊断时间,但需要大量的标记数据.
    • 众包提供了一种解决方案,可以从不同的注释者专业水平获得标签.

    研究的目的:

    • 评估乳腺癌组织分类的多注释器方法.
    • 为了比较两个损失函数的性能:交叉s- (RCDNN) 和通用交叉 (GCEDL).
    • 在不同注释者专业知识的场景中评估模型的稳定性.

    主要方法:

    • 利用乳腺癌组织数据集,由专家和非专家注释者进行注释.
    • 实现并比较两个不同的损失函数:RCDNN和GCEDL.
    • 在多注释器设置中评估模型性能.

    主要成果:

    • 多注释器场景在实现高诊断准确性方面提出了重大挑战.
    • 与RCDNN相比,通用的交叉损失函数 (GCEDL) 模型表现出优越的稳定性.
    • GCEDL模型的性能与黄金标准的单注释器模型相提并论.

    结论:

    • 多注释器数据可以用于乳腺癌分类,但仔细的模型选择至关重要.
    • 在医学图像分析中,GCEDL显示出处理来自各种注释器的噪音标签的前景.
    • 未来的工作应该专注于进一步优化多注释器数据集的模型,以提高CAD系统的可靠性.