Fine-Grained Breast Cancer Classification With Bilinear Convolutional Neural Networks (BCNNs) - Liu, Weihuang; Juhas, Mario; Zhang, Yang (Frontiers in Genetics, 2020)
Classification of histopathological images from cancers is a challenging task, even for well-trained professionals, due to the fine-grained variability of the disease. Deep Convolutional Neural Networks (CNNs) have shown great potential for many highly variable fine-grained objects. In this study, we proposed a Bilinear Convolutional Neural Networks (BCNNs) based deep learning method for fine-grained classification of breast cancer histopathological images. We evaluated our model by comparing with several deep learning algorithms for fine-grained classification. We used bilinear pooling to aggregate a large number of orderless features without consideration of the disease location. The experimental results on BreaKHis, a publicly available breast cancer dataset, showed that the proposed method is cutting-edge with accuracies of 99.24% in binary classification and 95.95% in fine-grained classification.