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Related Experiment Video

Updated: Jun 19, 2025

Building Up a High-throughput Screening Platform to Assess the Heterogeneity of HER2 Gene Amplification in Breast Cancers

Building Up a High-throughput Screening Platform to Assess the Heterogeneity of HER2 Gene Amplification in Breast Cancers

Published on: December 5, 2017

Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling.

Sahan Yoruc Selcuk^1,2,3, Xilin Yang^1,2,3, Bijie Bai^1,2,3

¹Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.

|July 24, 2024

Summary

This summary is machine-generated.

A new deep learning method automates Human Epidermal Growth Factor Receptor 2 (HER2) status classification in breast cancer (BC) images. This AI tool enhances diagnostic precision and evaluation speed for improved cancer treatment planning.

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Last Updated: Jun 19, 2025

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Area of Science:

Oncology
Computational Pathology
Biomedical Imaging

Background:

Accurate Human Epidermal Growth Factor Receptor 2 (HER2) assessment is crucial for breast cancer (BC) prognosis and treatment.
Manual HER2 evaluation in immunohistochemistry (IHC) slides faces challenges like observer variability and delays.

Purpose of the Study:

To develop and validate a deep learning approach for automated HER2 status classification in IHC-stained BC tissue images.
To improve the efficiency and consistency of HER2 testing in breast cancer diagnostics.

Main Methods:

A deep learning model employing pyramid sampling to analyze morphological features at multiple spatial scales.
The method efficiently processes images, considering both cellular and tissue-level details for comprehensive analysis.

Main Results:

The automated system achieved an 84.70% classification accuracy on a dataset of 523 core tissue microarray images.
The approach effectively handles HER2 expression heterogeneity in breast cancer tissues.

Conclusions:

The developed deep learning system shows potential as a reliable adjunct tool for pathologists.
This automated approach can enhance diagnostic accuracy, speed up evaluation, and impact breast cancer treatment strategies.