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Immunocytochemistry and Immunohistochemistry

Immunocytochemistry and Immunohistochemistry

Immunocytochemistry (ICC) and immunohistochemistry (IHC) are techniques that use antibodies to check for specific proteins or antigens in a sample. The technique was first published by Albert Coons in 1941 to detect the presence of pneumococcal antigen in tissue sections from mice infected with Pneumococcus. Immunocytochemistry helps localization of proteins or antigens in individual cells like blood cells, stem cells, etc., while immunohistochemistry does the same for tissue samples.
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DeepHistoClass: A Novel Strategy for Confident Classification of Immunohistochemistry Images Using Deep Learning.

Biraja Ghoshal¹, Feria Hikmet², Charles Pineau³

¹Department of Computer Science, Brunel University London, Uxbridge, United Kingdom.

Molecular & Cellular Proteomics : MCP

|August 23, 2021

Summary

This study introduces a new AI framework for automated annotation of immunohistochemistry (IHC) images, significantly improving diagnostic accuracy for protein mapping. The developed confidence score also helps identify potential errors in manual annotations.

Keywords:

artificial intelligence histology immunohistochemistry machine learning testis

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Area of Science:

Computational biology
Digital pathology
Proteomics

Background:

Global efforts aim to create a human body reference map for health and medicine.
Antibody-based proteomics via immunohistochemistry (IHC) integrates with single-cell transcriptomics.
Manual annotation of IHC images is subjective and resource-intensive.

Purpose of the Study:

To present a reliable framework for automated annotation of IHC images.
To develop an AI model for accurate pattern recognition in IHC data.
To introduce a confidence metric for AI predictions in digital pathology.

Main Methods:

Developed a multilabel classification framework for 7848 human testis IHC images.
Utilized manual annotation data for 2794 unique proteins across eight cell types.
Trained a Hybrid Bayesian Neural Network incorporating a novel uncertainty metric (DeepHistoClass Confidence Score).

Main Results:

The AI framework achieved an average diagnostic performance of 96.3%, an improvement from 86.9%.
The DeepHistoClass Confidence Score enhances classification reliability and aids in identifying manual annotation errors.
The developed workflow shows potential for broader applications in digital pathology and protein mapping.

Conclusions:

The proposed AI framework offers a reliable and efficient method for automated IHC image annotation.
The integrated confidence metric improves AI diagnostic performance and quality control.
This approach has significant implications for large-scale biological projects like the Human Protein Atlas and digital pathology.