A new transfer learning based approach to magnification dependent and independent classification of breast cancer in histopathological images.

• A new transfer learning based approach for the classification of breast cancer in histopathological images is proposed. • It can handle both magnification dependent (MI) and independent (MI) binary and eight-class classifications simultaneously. • The residual CNN ResNet-18 is investigated as a backbone model with block-wise fine-tuning strategy. • GCN from the target's task values and three-fold data augmentation are exploited to boost the classification performance. • The proposed approach outperforms 11 recent state-of-the-art MD and MI counterparts by a fair margin. The visual analysis of histopathological images is the gold standard for diagnosing breast cancer, yet a strenuous and an intricate task that requires years of pathologist training. Therefore, automating this task using computer-aided diagnosis (CAD) is highly expected. This paper proposes a new transfer learning-based approach to automated classification of breast cancer from histopathological images, including magnification dependent (MD) and magnification independent (MI) binary and eight-class classifications. We apply the deep neural network ResNet-18 to this problem, which is pre-trained on ImageNet, a large dataset of common images. We then design our transfer learning method to refine the network on histopathological images. Our transfer learning method is based on block-wise fine-tuning strategy; in which we make the last two residual blocks of the deep network model more domain-specific to our target data. It substantially helps to avoid over-fitting and speed up the training. Furthermore, we strengthen the adaptability of the proposed approach by using global contrast normalization (GCN) based on the target's data values and three-fold data augmentation on training data. The experimental results of MD and MI binary and eight-class classifications on the publicly available BreaKHis dataset demonstrate that our approach is promising and effective, outperforming recent state-of-the-art MD and MI counterparts by a fair margin. [ABSTRACT FROM AUTHOR]