  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.
