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Related Experiment Video

Updated: Feb 2, 2026

Elastic Staining on Paraffin-embedded Slides of pT3N0M0 Gastric Cancer Tissue

Elastic Staining on Paraffin-embedded Slides of pT3N0M0 Gastric Cancer Tissue

Published on: May 1, 2019

Cell Classification in ER-Stained Whole Slide Breast Cancer Images Using Convolutional Neural Network.

Mohammad F Jamaluddin, Mohammad F A Fauzi, Fazly S Abas

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 17, 2018

Summary

This summary is machine-generated.

This study introduces a convolutional neural network (CNN) to classify estrogen receptor (ER) staining in breast cancer cells. The AI system accurately predicts hormone receptor status, aiding treatment decisions for breast carcinoma patients.

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Area of Science:

Oncology
Computational Pathology
Artificial Intelligence in Medicine

Background:

Hormone receptor status is crucial for guiding breast cancer treatment decisions.
Estrogen receptor (ER) positivity indicates potential response to hormonal therapy.

Purpose of the Study:

To develop and evaluate a convolutional neural network (CNN) system for classifying ER-stained breast carcinoma cells.
To assess the accuracy of the CNN in determining ER status based on staining intensity.

Main Methods:

Utilized a CNN multiclass classifier to analyze ER-stained whole slide images of breast carcinoma.
Trained and tested the system on a dataset of 1200 cells to classify staining strength.

Main Results:

The proposed CNN achieved an overall accuracy of 88.8% for cell classification.
The system demonstrated a high Area Under the Curve (AUC) score of 97.5%.

Conclusions:

The developed CNN system shows significant promise for automated hormone receptor testing in breast cancer.
Accurate ER status classification by AI can assist clinicians in selecting appropriate hormonal therapies.