  Throughout the world, breast cancer is one of the leading causes of female
death. Recently, deep learning methods are developed to automatically grade
breast cancer of histological slides. However, the performance of existing deep
learning models is limited due to the lack of large annotated biomedical
datasets. One promising way to relieve the annotating burden is to leverage the
unannotated datasets to enhance the trained model. In this paper, we first
apply active learning method in breast cancer grading, and propose a
semi-supervised framework based on expectation maximization (EM) model. The
proposed EM approach is based on the collaborative filtering among the
annotated and unannotated datasets. The collaborative filtering method
effectively extracts useful and credible datasets from the unannotated images.
Results of pixel-wise prediction of whole-slide images (WSI) demonstrate that
the proposed method not only outperforms state-of-art methods, but also
significantly reduces the annotation cost by over 70%.
