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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 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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Related Experiment Video

Updated: May 9, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Unsupervised single-domain generalization for tissue classification via progressive domain transformation.

Jiatai Lin1, Qian Li2, Yanfen Cui3

  • 1Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou 510080, China.

Medical Image Analysis
|May 7, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an unsupervised progressive generalization framework for tissue classification in digital pathology. The method enhances model generalization across different data sources and scanners, improving diagnostic accuracy.

Keywords:
Single-domain generalizationTissue classificationUnsupervised learning

Related Experiment Videos

Last Updated: May 9, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

  • Computational pathology
  • Digital pathology
  • Medical imaging analysis

Background:

  • Tissue classification is crucial in computational pathology.
  • Domain shifts in digital pathology images hinder model generalization.
  • Existing research often uses multiple public datasets to test generalization.

Purpose of the Study:

  • To address domain generalization challenges in tissue classification.
  • To introduce a novel unsupervised single-domain progressive generalization (USD-PG) framework.
  • To evaluate the proposed framework on new and existing datasets.

Main Methods:

  • Proposed an unsupervised single-domain progressive generalization (USD-PG) framework.
  • Incorporated style progressive data transformation (Style-PDT) and spatial progressive data transformation (Spatial-PDT).
  • Evaluated on the GDPH-CRC-HE-MS and NCT-CRC-HE-100K datasets.

Main Results:

  • USD-PG demonstrated superior performance in single-source domain generalization for tissue classification.
  • The framework effectively handled scanner-based and data-source domain shifts.
  • Achieved enhanced generalization ability on colorectal cancer histology images.

Conclusions:

  • USD-PG offers a promising approach for improving domain generalization in tissue classification.
  • The framework has potential applicability in clinical settings for computational pathology.
  • The study highlights the effectiveness of progressive data transformations in handling domain shifts.