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.

翻译：乳腺癌（BC）的早期诊断对全球范围内降低死亡率具有显著贡献。检测不同因素和生物标志物，如雌激素受体（ER）、孕激素受体（PR）、人表皮生长因子受体2（HER2）基因、组织学分级（HG）、腋窝淋巴结（ALN）状态以及分子亚型（MS），可在改善乳腺癌诊断中发挥重要作用。然而，现有方法仅能预测单一因素，这使其不太适用于诊断及制定治疗策略。本文提出利用H&E染色全切片图像（WSI）对早期乳腺癌诊断的六个关键指示因素（ER、PR、HER2、ALN、HG、MS）进行分类。为精确捕捉局部邻域关系，我们利用来自WSI恶性区域大尺寸图像块的空间域与频域信息。此外，为适应不同尺寸的兴趣区域数量并对每个区域给予充分关注，我们提出了一种恶性区域学习注意力网络。实验结果表明，在公开数据集上，通过恶性区域学习模块结合空间与频域信息，能显著提升多因素及单因素分类性能。