AUTHOR=Liu Weihuang , Juhas Mario , Zhang Yang TITLE=Fine-Grained Breast Cancer Classification With Bilinear Convolutional Neural Networks (BCNNs) JOURNAL=Frontiers in Genetics VOLUME=11 YEAR=2020 URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2020.547327 DOI=10.3389/fgene.2020.547327 ISSN=1664-8021 ABSTRACT=

Classification of histopathological images of cancer is challenging even for well-trained professionals, due to the fine-grained variability of the disease. Deep Convolutional Neural Networks (CNNs) showed great potential for classification of a number of the highly variable fine-grained objects. In this study, we introduce a Bilinear Convolutional Neural Networks (BCNNs) based deep learning method for fine-grained classification of breast cancer histopathological images. We evaluated our model by comparison with several deep learning algorithms for fine-grained classification. We used bilinear pooling to aggregate a large number of orderless features without taking into consideration the disease location. The experimental results on BreaKHis, a publicly available breast cancer dataset, showed that our method is highly accurate with 99.24% and 95.95% accuracy in binary and in fine-grained classification, respectively.