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

Fixation and Sectioning

Fixation and Sectioning

Two basic types of preparation are used to visualize specimens with a light microscope: wet mounts and fixed specimens.
The simplest type of preparation is the wet mount, in which the specimen is placed in a drop of liquid on the slide. A liquid specimen can be directly deposited on the slide using a dropper. Solid specimens, such as skin scraping, can be placed on the slide before adding a drop of liquid to prepare the wet mount. Sometimes the liquid is simply water, but stains are often added...

Differential Staining Technique

Differential Staining Technique

Differential staining is an essential microbiological technique that exploits variations in cell wall structures to classify and identify microorganisms. It facilitates the distinction of bacteria, aiding in diagnostic and research applications. Two of the most widely used differential staining methods are Gram staining and acid-fast staining, both of which rely on the chemical and structural differences in bacterial cell walls.Gram Staining TechniqueGram staining differentiates bacteria by...

Simple Staining Technique

Simple Staining Technique

OverviewStaining techniques in microscopy enhance the visualization of microorganisms by increasing contrast and allowing the differentiation of cellular structures. Simple staining is one of the fundamental methods used to observe the basic morphological characteristics of microorganisms, including their size, shape, and arrangement. This method relies on the application of a single dye to stain the entire cell, producing a clear contrast between the cell and the background.FixationFixation is...

You might also read

Related Articles

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

Sort by

Same author

Translation of frozen sections into FFPE images for skin cancer resection margins using generative AI.

NPJ digital medicine·2026

Same author

Mullerian Adenosarcomas with Extrauterine Involvement, Sarcomatous Overgrowth and <i>MDM2</i> Amplification: A Diagnostic Challenge.

International journal of surgical pathology·2026

Same author

Novel Pattern of Nuclear Staining With ROS1 Immunohistochemistry: A Case Report.

Case reports in pulmonology·2026

Same author

Two-Step Ensemble Convolutional Neural Networks for Colonoscopic Biopsy Classification Resembling Pathologists' Process.

Journal of Korean medical science·2026

Same author

Self-supervised contrastive learning enables robust electrocardiogram-based cardiac classification.

Heart rhythm O2·2026

Same author

Shift happens: a fairness-oriented framework for medical classification under hidden bias.

International journal of computer assisted radiology and surgery·2026

Search research articles

Related Experiment Video

Updated: Sep 14, 2025

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

CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image

Bodong Zhang¹, Hamid Manoochehri¹, Man Minh Ho²

¹Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, USA; Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.

Medical Image Analysis

|July 18, 2025

Summary

This summary is machine-generated.

This study introduces CLASS-M, a semi-supervised model for patch-level histopathological image classification. It achieves top performance on clear cell renal cell carcinoma datasets without needing extensive labels.

Keywords:

Contrastive learning Digital histopathological images Pseudo-labeling Semi-supervised learning Stain separation

More Related Videos

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Published on: June 21, 2024

Related Experiment Videos

Last Updated: Sep 14, 2025

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

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Published on: June 21, 2024

Area of Science:

Digital pathology
Medical image analysis
Computational biology

Background:

Histopathological image classification is crucial in medical analysis.
Weakly supervised learning is common due to readily available case-level labels.
Patch-level classification is vital for limited datasets or critical local predictions, but requires extensive labeled data.

Purpose of the Study:

To propose a novel semi-supervised model, CLASS-M, for patch-level histopathological image classification.
To address the challenge of acquiring extensively labeled datasets for training.
To improve classification accuracy in scenarios with limited or localized label availability.

Main Methods:

Developed CLASS-M, a semi-supervised model for patch-level histopathological image classification.
Employed a contrastive learning module utilizing adaptive stain separation for Hematoxylin and Eosin images.
Integrated a pseudo-labeling module with MixUp data augmentation.

Main Results:

CLASS-M demonstrated superior performance compared to state-of-the-art models on two clear cell renal cell carcinoma datasets.
The model effectively performs patch-level classification without requiring large-scale labeled datasets.
Achieved the best performance metrics across both evaluated datasets.

Conclusions:

CLASS-M offers an effective semi-supervised approach for patch-level histopathological image classification.
The model overcomes limitations associated with extensive data labeling requirements.
This method shows significant potential for improving diagnostic accuracy in digital pathology.