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.

该研究提出了一种集成方法，结合 HER2 表达的多分类，从乳腺癌组织病理切片图像中诊断乳腺癌，通过与现有模型的比较，该方法取得了 97.12% 的准确率，并有显著改进。

应对 HER2 乳腺癌组织学图像分类中的不确定性：一种可解释的集成方法与阈值筛选的单实例评估（SIE）

Addressing Uncertainty in Imbalanced Histopathology Image Classification  of HER2 Breast Cancer: An interpretable Ensemble Approach with Threshold  Filtered Single Instance Evaluation (SIE)

The evaluation of human epidermal growth factor receptor 2 (HER2) expression
is essential to formulate a precise treatment for breast cancer. The routine
evaluation of HER2 is conducted with immunohistochemical techniques (IHC),
which is very expensive. Therefore, for the first time, we propose a breast
cancer immunohistochemical (BCI) benchmark attempting to synthesize IHC data
directly with the paired hematoxylin and eosin (HE) stained images. The dataset
contains 4870 registered image pairs, covering a variety of HER2 expression
levels. Based on BCI, as a minor contribution, we further build a pyramid
pix2pix image generation method, which achieves better HE to IHC translation
results than the other current popular algorithms. Extensive experiments
demonstrate that BCI poses new challenges to the existing image translation
research. Besides, BCI also opens the door for future pathology studies in HER2
expression evaluation based on the synthesized IHC images. BCI dataset can be
downloaded from this https URL

提出了一种乳腺癌免疫组化标准，能够通过 HE 染色图像的合成直接综合 IHC 数据，可用于 HER2 表达评估的病理研究。