Abdel-Hamid Lamiaa, Yogarajah Pratheepan, Tealab Safy Hosny Ahmed
Electronics and Communication Department, Faculty of Engineering, Misr International University, Cairo, Egypt.
School of Computing, Engineering and Intelligent Systems, University of Ulster, Londonderry, United Kingdom.
PLoS One. 2025 Sep 15;20(9):e0332362. doi: 10.1371/journal.pone.0332362. eCollection 2025.
Human epidermal growth factor receptor 2 (HER2)-positive breast cancer is an aggressive cancer type that requires special diagnosis and treatment methods. Immunohistochemistry (IHC) staining effectively highlights relevant morphological structures within histopathological images yet can be expensive in terms of both labor and required laboratory equipment. Hematoxylin and eosin (H&E) images are more readily available and less expensive than IHC images as they are routinely performed for all patient samples. Lightweight models are well-suited for deployment on resource-constrained devices such as mobile phones and embedded systems, making them ideal for real-time diagnosis in rural regions and developing countries. In this study, IHC images are compared to H&E images for automatic HER2 scoring using lightweight deep models that incorporate several advanced techniques including network pruning, domain adaptation, and attention mechanisms. Two lightweight models are presented: PrunEff4 and ATHER2. PrunEff4 is a subset of EfficientNetV2B0 pruned to reduce the network parameters by ~80%. ATHER2 is a customized lightweight network that employs different sized convolutional filters along with a convolutional block attention module (CBAM). For PrunEff4 and ATHER2, transfer learning (pretraining on ImageNet) and domain-specific pretraining were employed, respectively. Different datasets were utilized in the development and final testing phases in order to effectively evaluate their generalization capability. In all experiments, both networks resulted in accuracies ranging from 97% to 100% for binary classifications and from 95.5% to 98.5% for multiclass classifications regardless of whether IHC or H&E images were utilized. Network pruning significantly reduced the network parameters whilst maintaining reliable performance. Domain-specific pretraining significantly enhanced performance, particularly in complex classification tasks such as HER2 scoring using H&E images and multiclass classifications. Both IHC and H&E stained images were suitable for deep learning-based HER2 scoring, given that the deep networks are efficiently trained for the specified task.
人表皮生长因子受体2（HER2）阳性乳腺癌是一种侵袭性癌症类型，需要特殊的诊断和治疗方法。免疫组织化学（IHC）染色能有效突出组织病理学图像中的相关形态结构，但在人力和所需实验室设备方面成本较高。苏木精和伊红（H&E）图像比IHC图像更容易获得且成本更低，因为它们是对所有患者样本常规进行的。轻量级模型非常适合部署在资源受限的设备上，如手机和嵌入式系统，这使其成为农村地区和发展中国家进行实时诊断的理想选择。在本研究中，使用结合了网络剪枝、域适应和注意力机制等多种先进技术的轻量级深度模型，将IHC图像与H&E图像进行比较，以实现HER2的自动评分。提出了两个轻量级模型：PrunEff4和ATHER2。PrunEff4是EfficientNetV2B0的一个子集，经过剪枝以将网络参数减少约80%。ATHER2是一个定制的轻量级网络，采用不同大小的卷积滤波器以及卷积块注意力模块（CBAM）。对于PrunEff4和ATHER2，分别采用了迁移学习（在ImageNet上进行预训练）和特定领域预训练。在开发和最终测试阶段使用了不同的数据集，以有效评估它们的泛化能力。在所有实验中，无论使用IHC图像还是H&E图像，两个网络在二分类中的准确率范围为97%至100%，在多分类中的准确率范围为95.5%至98.5%。网络剪枝在保持可靠性能的同时显著减少了网络参数。特定领域预训练显著提高了性能，特别是在复杂的分类任务中，如使用H&E图像进行HER2评分和多分类。鉴于深度网络针对指定任务进行了有效训练，IHC和H&E染色图像都适用于基于深度学习的HER2评分。
