Breast Cancer (BC) is among women's most lethal health concerns. Early diagnosis can alleviate the mortality rate by helping patients make efficient treatment decisions. Human Epidermal Growth Factor Receptor (HER2) has become one the most lethal subtype of BC. According to the College of American Pathologists American Society of Clinical Oncology (CAP/ASCO), the severity level of HER2 expression can be classified between 0 and 3+ range. HER2 can be detected effectively from immunohistochemical (IHC) and, hematoxylin & eosin (HE) images of different classes such as 0, 1+, 2+, and 3+. An ensemble approach integrated with threshold filtered single instance evaluation (SIE) technique has been proposed in this study to diagnose BC from the multi-categorical expression of HER2 subtypes. Initially, DenseNet201 and Xception have been ensembled into a single classifier as feature extractors with an effective combination of global average pooling, dropout layer, dense layer with a swish activation function, and l2 regularizer, batch normalization, etc. After that, extracted features has been processed through single instance evaluation (SIE) to determine different confidence levels and adjust decision boundary among the imbalanced classes. This study has been conducted on the BC immunohistochemical (BCI) dataset, which is classified by pathologists into four stages of HER2 BC. This proposed approach known as DenseNet201-Xception-SIE with a threshold value of 0.7 surpassed all other existing state-of-art models with an accuracy of 97.12%, precision of 97.15%, and recall of 97.68% on H&E data and, accuracy of 97.56%, precision of 97.57%, and recall of 98.00% on IHC data respectively, maintaining momentous improvement. Finally, Grad-CAM and Guided Grad-CAM have been employed in this study to interpret, how TL-based model works on the histopathology dataset and make decisions from the data.

翻译：乳腺癌（BC）是女性最致命的健康威胁之一。早期诊断可通过帮助患者制定有效治疗方案来降低死亡率。人类表皮生长因子受体2（HER2）已成为BC最具侵袭性的亚型之一。根据美国病理学家学会/美国临床肿瘤学会（CAP/ASCO）标准，HER2表达严重程度可分级为0至3+。通过免疫组化（IHC）及苏木精-伊红（HE）染色图像，可有效检测0、1+、2+和3+等不同类别的HER2表达。本研究提出一种集成阈值过滤单实例评估（SIE）技术的集成方法，用于多类别HER2亚型表达下的BC诊断。首先，将DenseNet201与Xception集成为单一分类器，并通过全局平均池化、Dropout层、Swish激活函数的全连接层、L2正则化及批归一化等模块的组合作为特征提取器。随后，对提取的特征通过单实例评估（SIE）确定不同置信度水平，并调整类别不平衡分类中的决策边界。本研究在经病理学家标注为四阶段HER2 BC的BCI数据集上开展实验。所提出的DenseNet201-Xception-SIE方法在阈值为0.7时，在HE数据上分别达到97.12%的准确率、97.15%的精确率和97.68%的召回率；在IHC数据上分别达到97.56%的准确率、97.57%的精确率和98.00%的召回率，显著超越现有最优模型。最后，本研究采用Grad-CAM与引导式Grad-CAM进行可解释性分析，阐明基于迁移学习的模型在组织病理数据集上的工作机制与决策依据。