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相关概念视频

Classification of Epithelial Tissues: Overview01:22

Classification of Epithelial Tissues: Overview

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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,...
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Classification of Epithelial Tissues: Glandular Epithelium01:20

Classification of Epithelial Tissues: Glandular Epithelium

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The glandular epithelium is made of one or more epithelial cells modified to synthesize and secrete chemical substances. Glandular epithelia can be classified based on cell number. Unicellular glands have individual secretory cells scattered across the epithelial monolayer. In contrast, multicellular glands consist of a hollow tubular duct attached to the cluster of secretory cells located in the deep pockets.
Multicellular glands are formed during early development when epithelial budding...
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Classification of Epithelial Tissues: Stratified Epithelium01:29

Classification of Epithelial Tissues: Stratified Epithelium

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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...
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Skin Cancer01:30

Skin Cancer

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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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相关实验视频

Updated: Jul 17, 2025

Author Spotlight: Genetically Engineered Mouse Models and Pathological Characterization of Neurofibromatosis Type 1 Associated Tumors
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针对恶性瘤分级系统的基于域和组织病理学适应的分类.

Vicky Mudeng1, Mifta Nur Farid2, Gelan Ayana3

  • 1Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea; Department of Electrical Engineering, Institut Teknologi Kalimantan, Balikpapan, Indonesia.

The American journal of pathology
|September 6, 2023
PubMed
概括

这项研究引入了一种自动化系统,用于使用卷积神经网络对乳腺癌的侵入性导管癌进行分级. 这种新的方法实现了高精度,改进了手动病理学,以便更好地做出治疗决策.

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Building Up a High-throughput Screening Platform to Assess the Heterogeneity of HER2 Gene Amplification in Breast Cancers
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Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions
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Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions

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Building Up a High-throughput Screening Platform to Assess the Heterogeneity of HER2 Gene Amplification in Breast Cancers
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科学领域:

  • 在瘤学瘤学.
  • 计算机科学 计算机科学
  • 医疗成像医学成像

背景情况:

  • 从组织病理学幻灯片手动分类乳腺癌是主观的,资源密集的.
  • 准确的扩散率量化对于有效的乳腺癌治疗计划至关重要.
  • 卷积神经网络 (CNN) 为病理学中的客观和高效的图像分析提供了潜力.

研究的目的:

  • 开发一种新的,自动化方案,用于对侵袭性导管癌中的乳腺癌恶性病变进行分级.
  • 通过基于计算机的观察员来增强临床医生的诊断能力.
  • 为了创建一个完整的分级系统,用于依赖放大和独立分类.

主要方法:

  • 采用多阶段的转移学习与域和病理学转换用于分类.
  • 使用预训练模型 (InceptionResNetV2,InceptionV3,NASNet-Large,ResNet50,ResNet101,VGG19,Xception) 在BreaKHis数据集上进行域调整 (×40放大).
  • 选择InceptionV3和Xception来将Databiox数据库分类为1,2或3等级,利用它们的预训练重量.

主要成果:

  • 在放大依赖分类中取得了出色的性能,整体精度从90.17%±3.08%到97.67%±1.09%不等.
  • 报告的F1分数在0.9013和0.9760之间,以放大依赖的分类.
  • 在不同的数据集和放大级别上证明了拟议的自动分级系统的有效性.

结论:

  • 提出的自动化乳腺癌分级系统显示出高准确性和可靠性.
  • 这种方法可以帮助开发出可靠的乳腺癌分级系统,用于临床应用.
  • 这种无补丁的,基于CNN的方法为手动组织病理学评估提供了一个有希望的替代方案.