Automated HER2 scoring with uncertainty quantification using lensfree holography and deep learning

2026-01-26

AI Summary

This study addresses the limitations of conventional HER2 scoring, which relies on expensive and bulky optical equipment and lacks reliable uncertainty quantification, thereby hindering deployment in resource-limited settings. To overcome these challenges, this work proposes a high-throughput, low-cost, and portable automated HER2 scoring method that integrates lensless holographic imaging with uncertainty-aware deep learning. The system employs RGB laser illumination for lensless holography and incorporates Bayesian Monte Carlo Dropout to quantify prediction uncertainty. Evaluated on a blind test set of 412 clinical samples, the method achieves an 84.9% accuracy in four-class classification and 94.8% in binary classification, with an overall calibration error of 30.4%, significantly enhancing diagnostic reliability and clinical applicability.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Other Foundations of Computer Vision
• Intelligent Robots: State Estimation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Accurate assessment of human epidermal growth factor receptor 2 (HER2) expression is critical for breast cancer diagnosis, prognosis, and therapy selection; yet, most existing digital HER2 scoring methods rely on bulky and expensive optical systems. Here, we present a compact and cost-effective lensfree holography platform integrated with deep learning for automated HER2 scoring of immunohistochemically stained breast tissue sections. The system captures lensfree diffraction patterns of stained HER2 tissue sections under RGB laser illumination and acquires complex field information over a sample area of ~1,250 mm^2 at an effective throughput of ~84 mm^2 per minute. To enhance diagnostic reliability, we incorporated an uncertainty quantification strategy based on Bayesian Monte Carlo dropout, which provides autonomous uncertainty estimates for each prediction and supports reliable, robust HER2 scoring, with an overall correction rate of 30.4%. Using a blinded test set of 412 unique tissue samples, our approach achieved a testing accuracy of 84.9% for 4-class (0, 1+, 2+, 3+) HER2 classification and 94.8% for binary (0/1+ vs. 2+/3+) HER2 scoring with uncertainty quantification. Overall, this lensfree holography approach provides a practical pathway toward portable, high-throughput, and cost-effective HER2 scoring, particularly suited for resource-limited settings, where traditional digital pathology infrastructure is unavailable.

Problem

• HER2 scoring
• digital pathology
• lensfree holography
• resource-limited settings
• automated diagnosis

Innovation

• lensfree holography
• deep learning
• HER2 scoring
• uncertainty quantification
• Bayesian Monte Carlo dropout