Early diagnosis of breast cancer (BC) significantly contributes to reducing
the mortality rate worldwide. The detection of different factors and biomarkers
such as Estrogen receptor (ER), Progesterone receptor (PR), Human epidermal
growth factor receptor 2 (HER2) gene, Histological grade (HG), Auxiliary lymph
node (ALN) status, and Molecular subtype (MS) can play a significant role in
improved BC diagnosis. However, the existing methods predict only a single
factor which makes them less suitable to use in diagnosis and designing a
strategy for treatment. In this paper, we propose to classify the six essential
indicating factors (ER, PR, HER2, ALN, HG, MS) for early BC diagnosis using
H\&E stained WSI's. To precisely capture local neighboring relationships, we
use spatial and frequency domain information from the large patch size of WSI's
malignant regions. Furthermore, to cater the variable number of regions of
interest sizes and give due attention to each region, we propose a malignant
region learning attention network. Our experimental results demonstrate that
combining spatial and frequency information using the malignant region learning
module significantly improves multi-factor and single-factor classification
performance on publicly available datasets.

通过使用组织病理学图像检测和结合多种因素（ER、PR、HER2、ALN、HG、MS）进行分类，本研究展示了在公开数据集上，利用恶性区域学习模块结合空间和频域信息，可显著提高乳腺癌的多因素和单因素诊断性能。