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Automated Her2 Scoring With Uncertainty Quantification Using Lensfree Holography And Deep Learning.

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Automated Her2 Scoring With Uncertainty Quantification Using Lensfree Holography And Deep Learning.

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Automated HER2 Scoring with Uncertainty Quantification Using Lensfree Holography and Deep Learning.

Che-Yung Shen^1,2,3, Xilin Yang^1,2,3, Yuzhu Li^1,2,3

¹Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA 90095, USA.

|June 18, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

We developed an automated system for scoring human epidermal growth factor receptor 2 (HER2) in breast cancer using lensfree holography and deep learning. This cost-effective method offers rapid and reliable HER2 assessment, especially in resource-limited settings.

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Area of Science:

Biomedical Optics
Digital Pathology
Machine Learning in Medicine

Background:

Accurate human epidermal growth factor receptor 2 (HER2) scoring is crucial for breast cancer management.
Current digital HER2 scoring methods often require expensive and bulky optical systems.
There is a need for cost-effective and accessible HER2 assessment tools.

Purpose of the Study:

To develop an automated HER2 scoring system utilizing lensfree holography.
To integrate deep learning for rapid and accurate HER2 classification.
To create a cost-effective alternative to traditional digital pathology systems.

Main Methods:

Lensfree holography device capturing diffraction patterns of stained HER2 tissue sections.
Deep learning model incorporating Bayesian Monte Carlo dropout for uncertainty quantification.

High-throughput data acquisition with a sample area of ~1,250 mm² and throughput of ~84 mm²/minute.

Main Results:

Achieved 84.9% accuracy for 4-class HER2 classification (0, 1+, 2+, 3+).
Attained 94.8% accuracy for binary HER2 scoring (0/1+ vs. 2+/3+) with uncertainty quantification.
Demonstrated robust and reliable scoring through autonomous uncertainty estimates.

Conclusions:

Lensfree holography with deep learning offers a practical solution for automated HER2 scoring.
The system is highly suitable for resource-limited settings lacking traditional digital pathology infrastructure.
This approach facilitates high-throughput and cost-effective breast cancer diagnostics.