Multi-classification of Breast Cancer Histology Images by Using Gravitation Loss

The scarcity of professional doctors stimulates the progress of breast cancer classification. However, there are still numerous challenges such as varied appearances (color, texture etc.) of microscopy images and the ambiguous category boundaries. In this paper, we propose an efficient and effective method to achieve multi-classification for H&E stained breast cancer images. Firstly, to restrain color noises in the staining stage, data augmentation in HSV color space is used to increase the diversity of color distribution. In addition, inspired by the principle of gravitation, a Gravitation Loss (G-loss) is proposed to maximize inter-class difference and minimize intra-class variance. The experimental results on public BACH 2018 dataset indicate that the proposed algorithm achieves the state-of-the-art performance, which demonstrates its effectiveness.