Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Classification of Epithelial Tissues: Overview

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: Glandular Epithelium

Classification of Epithelial Tissues: Glandular Epithelium

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...

Classification of Leukocytes

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Correction: IgE-based analysis of sensitization and cross-reactivity to yellow mealworm and edible insect allergens before their widespread dietary introduction.

Scientific reports·2026

Same author

The insight into house dust mites: which Dermatophagoides pteronyssinus proteins and cross-reactive allergens play pivotal role in sensitization among Polish patients with suspicion of allergy?

Advances in medical sciences·2026

Same author

Does Previous Anaphylaxis Determine Differences Between Patients Undergoing Oral Food Challenges to Cow's Milk and Hen's Egg?

Nutrients·2026

Same author

Correct answer to the quiz. Check your diagnosis Cellular dermatofibroma.

Polish journal of pathology : official journal of the Polish Society of Pathologists·2026

Same author

Dyslipidemia Assessed in Pediatric Patients: Validation of LDL-C Assessed by Friedewald Formula, Direct Assessment, and Sampson-NIH Formula.

Diagnostics (Basel, Switzerland)·2025

Same author

Three Years After COVID-19 Vaccination, Anti-Spike SARS-CoV-2 Antibody Concentration Decreases and Is Accompanied by Increasing Anti-Nucleocapsid Seropositivity.

Viruses·2025

Search research articles

Related Experiment Video

Updated: Jun 30, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

DiagSet: a dataset for prostate cancer histopathological image classification.

Michał Koziarski^1,2,3, Bogusław Cyganek^4,5, Przemysław Niedziela⁵

¹Diagnostyka Consilio Sp. z o.o., Ul. Kosynierów Gdyńskich 61a, 93-357, Łódż, Poland. michal.koziarski@gmail.com.

Scientific Reports

|March 22, 2024

Summary

This study introduces a new prostate cancer dataset and a machine learning framework. The framework achieves 94.6% accuracy in detecting cancerous tissue, aiding in cancer diagnosis.

More Related Videos

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

Author Spotlight: Advancing Prostate Cancer Research Through Improved Tissue Sampling and Biobanking

Author Spotlight: Advancing Prostate Cancer Research Through Improved Tissue Sampling and Biobanking

Published on: November 17, 2023

Related Experiment Videos

Last Updated: Jun 30, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

Author Spotlight: Advancing Prostate Cancer Research Through Improved Tissue Sampling and Biobanking

Author Spotlight: Advancing Prostate Cancer Research Through Improved Tissue Sampling and Biobanking

Published on: November 17, 2023

Area of Science:

Oncology
Computational Pathology
Machine Learning

Background:

Prostate cancer detection presents significant challenges in histopathology.
Accurate and efficient diagnostic tools are crucial for patient outcomes.

Purpose of the Study:

To introduce a novel, large-scale histopathological dataset for prostate cancer detection.
To propose and evaluate a machine learning framework for identifying cancerous regions and predicting diagnoses from histopathological scans.

Main Methods:

Development of a comprehensive dataset with over 2.6 million tissue patches from annotated scans.
Implementation of an ensemble deep neural network framework for patch-level recognition and scan-level diagnosis.
Utilizing thresholding to manage uncertain cases and abstaining from decisions when necessary.

Main Results:

The proposed machine learning framework achieved 94.6% accuracy in patch-level cancer recognition.
Scan-level diagnosis predictions demonstrated high statistical agreement with human expert histopathologists.
The dataset is publicly available for further research and development.

Conclusions:

The novel dataset and machine learning framework show significant promise for improving prostate cancer detection accuracy.
The developed approach offers a robust tool for computational pathology, potentially assisting in clinical decision-making.
Further validation and application of this framework can enhance diagnostic efficiency and reliability in prostate cancer diagnosis.