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Updated: Aug 24, 2025

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

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Semi-Supervised Pixel Contrastive Learning Framework for Tissue Segmentation in Histopathological Image.

Jiangbo Shi, Tieliang Gong, Chunbao Wang

IEEE Journal of Biomedical and Health Informatics

|October 21, 2022

Summary

This summary is machine-generated.

This study introduces a semi-supervised pixel contrastive learning framework (SSPCL) for accurate histopathological image segmentation. SSPCL enhances tissue quantitation by effectively learning from unlabeled data and capturing cross-patch relationships, reducing labeling costs.

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Area of Science:

Digital Pathology
Computational Biology
Medical Image Analysis

Background:

Accurate histopathological image segmentation is crucial for precision pathology.
Large digital pathology image sizes necessitate tiling into smaller patches, limiting semantic information.
Existing methods struggle to model global semantic relationships within whole slide images.

Purpose of the Study:

To develop a semi-supervised framework (SSPCL) for accurate tissue segmentation in histopathological images.
To reduce the significant labeling costs associated with histopathological image analysis.
To effectively model semantic relationships across the entire digital slide.

Main Methods:

Proposed a semi-supervised pixel contrastive learning framework (SSPCL).
Introduced an uncertainty-guided mutual dual consistency learning module (UMDC) for unlabeled data utilization.
Implemented a cross-image pixel-contrastive learning module (CIPC) to capture inter-patch semantic relationships.
Developed novel domain-related sampling methods leveraging spatial patch structures for improved training efficiency.

Main Results:

SSPCL significantly reduces labeling costs for histopathological images.
The framework achieves accurate tissue quantitation.
Demonstrated superior performance on three tissue segmentation datasets, outperforming state-of-the-art methods by up to 5.0% in mDice.

Conclusions:

SSPCL effectively addresses the challenges of large image size and limited semantic information in histopathological image segmentation.
The proposed framework enhances learning from unlabeled data and captures crucial cross-patch semantic context.
SSPCL offers a promising solution for efficient and accurate tissue analysis in digital pathology.