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决策来降低死亡率。人表皮生长因子受体2（HER2）已成为BC中最致命的亚型之一。根据美国病理学家学会/美国临床肿瘤学会（CAP/ASCO）指南，HER2表达严重程度可分为0至3+等级。通过免疫组织化学（IHC）和苏木精-伊红（HE）图像，可有效检测不同类别（如0、1+、2+和3+）的HER2表达。本研究提出一种集成方法，结合阈值过滤单实例评估（SIE）技术，从HER2亚型的多类别表达中诊断乳腺癌。首先，将DenseNet201和Xception集成为单一分类器作为特征提取器，并采用全局平均池化、dropout层、含swish激活函数的密集层、l2正则化及批量归一化等有效组合。随后，通过单实例评估（SIE）处理提取的特征，以确定不同置信水平并调整不平衡类别间的决策边界。本研究基于乳腺癌免疫组织化学（BCI）数据集开展，该数据集由病理学家将HER2乳腺癌分为四个阶段。所提出的DenseNet201-Xception-SIE方法（阈值设为0.7）在H&E数据上以97.12%准确率、97.15%精确率和97.68%召回率，在IHC数据上以97.56%准确率、97.57%精确率和98.00%召回率，显著超越所有现有最先进模型。最后，采用Grad-CAM和引导式Grad-CAM解释基于迁移学习的模型在组织病理学数据集上的工作机制及决策过程。