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

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.

Automated summary

This paper proposes a new transfer learning-based approach to automated classification of breast cancer from histopathological images using ResNet-18. The method refines the deep neural network on histopathological images through block-wise fine-tuning strategy, achieving promising results on the BreaKHis dataset.