Abstract
When many histopathological breast images with different magnification levels need to be analyzed, diagnosing benign or malignant cancer from the images can be time-consuming. Automatic classification of histopathological images of breast cancer can support the diagnostic workflow in pathology, reducing analysis time. Recently, convolutional neural networks (CNNs) have been used for more accurate classification of breast cancer histopathological images. CNNs typically highlight semantic information to extract discriminative features. However, the traditional softmax loss used by CNNs usually lacks sufficient discriminative power. To address this problem, several angular margin-based softmax loss functions have been proposed, including Large Margin Softmax Loss (A-Softmax), Large Margin Cosine Loss (CosFace), Additive Angular Margin Loss (ArcFace), and Linear ArcFace (Li-ArcFace). All of these improved losses are based on the same concept: maximizing inter-class variance while minimizing intra-class distance. This paper focuses on these four losses and their effectiveness in extracting discriminative features and creating decision margins between classes. Extensive experimental evaluations were conducted on a public and well-known histopathological breast cancer image dataset (BreakHis). Further experiments with the BACH dataset for breast cancer classification and the SARS-CoV-2 CT-scan dataset for COVID-19 detection affirm the generalization capability of angular margin-based softmax losses in medical image classification.
