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Related Concept Videos

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...

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Related Experiment Video

Updated: Jun 25, 2026

Semi-automatic PD-L1 Characterization and Enumeration of Circulating Tumor Cells from Non-small Cell Lung Cancer Patients by Immunofluorescence
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A dual-branch deep learning network for circulating tumor cells classification.

Chao Han1,2, Jiaquan Lin2, Yanfang Liang3

  • 1Dongguan Key Laboratory of Medical Bioactive Molecular Developmental and Translational Research, Guangdong Provincial Key Laboratory of Medical Immunology and Molecular Diagnostics, Guangdong Medical University, Dongguan, 523808, China.

Journal of Translational Medicine
|September 24, 2025
PubMed
Summary

This study introduces a hybrid AI framework for accurately identifying circulating tumor cells (CTCs) in blood. The system achieves high accuracy, aiding prognosis and personalized therapy.

Keywords:
Circulating tumor cellsDeep learningFeature fusionFluorescent image

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Oncology

Background:

  • Circulating tumor cells (CTCs) are vital biomarkers for cancer prognosis and treatment monitoring.
  • Identifying CTCs is challenging due to their low abundance and heterogeneity.
  • Current deep learning models still face limitations in CTC detection.

Purpose of the Study:

  • To develop an advanced hybrid framework for improved CTC identification.
  • To enhance the robustness and accuracy of CTC detection using integrated methods.
  • To validate the clinical applicability of the automated CTC identification framework.

Main Methods:

  • A hybrid framework combining a dual-branch deep learning network with traditional image processing.
  • Integration of image and fluorescence attributes for enhanced feature representation.
  • Performance evaluation using accuracy, precision, recall, and comparison with manual pathological counting.

Main Results:

  • Achieved 97.05% accuracy in distinguishing CTCs from non-CTCs.
  • Demonstrated performance comparable to pathologists in survival prediction.
  • Dual-branch network and segmentation algorithms improved efficiency over conventional methods.
  • Clinical trials confirmed the framework's practicality for direct clinical use.

Conclusions:

  • The proposed framework significantly enhances CTC identification accuracy and efficiency.
  • Automated CTC identification results are directly applicable for prognosis, reducing manual intervention.
  • The framework shows strong clinical applicability and potential for advancing personalized cancer therapy.