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.

翻译：人表皮生长因子受体2（HER2）表达的准确评估对于乳腺癌的诊断、预后及治疗方案选择至关重要；然而，现有的大多数数字化HER2评分方法依赖于庞大且昂贵的光学系统。本文提出了一种集成深度学习的紧凑型、高性价比无透镜全息平台，用于对免疫组织化学染色的乳腺组织切片进行自动化HER2评分。该系统在RGB激光照明下捕获染色HER2组织切片的无透镜衍射图样，并在约1,250 mm^2的样本区域内以约84 mm^2/分钟的有效通量获取复场信息。为提高诊断可靠性，我们引入了一种基于贝叶斯蒙特卡洛Dropout的不确定性量化策略，该策略为每个预测提供自主的不确定性估计，支持可靠、稳健的HER2评分，总体校正率达到30.4%。在包含412个独特组织样本的盲法测试集上，我们的方法在四分类（0、1+、2+、3+）HER2分类中实现了84.9%的测试准确率，在二分类（0/1+ vs. 2+/3+）HER2评分（含不确定性量化）中实现了94.8%的准确率。总体而言，这种无透镜全息方法为实现便携式、高通量、高性价比的HER2评分提供了一条实用路径，尤其适用于传统数字病理学基础设施匮乏的资源有限环境。