Classification of Breast Cancer Histology Images Using Multi-Size and Discriminative Patches Based on Deep Learning

Abstract

The diagnosis of breast cancer histology images with hematoxylin and eosin stained is non-trivial, labor-intensive and often leads to a disagreement between pathologists. Computer-assisted diagnosis systems contribute to help pathologists improve diagnostic consistency and efficiency. With the recent advances in deep learning, convolutional neural networks (CNNs) have been successfully used for histology images analysis. The classification of breast cancer histology images into normal, benign, and malignant sub-classes is related to cells' density, variability, and organization along with overall tissue structure and morphology. Based on this, we extract both smaller and larger size patches from histology images, including cell-level and tissue-level features, respectively. However, there are some sampled cell-level patches that do not contain enough information that matches the image tag. Therefore, we propose a patches' screening method based on the clustering algorithm and CNN to select more discriminative patches. The approach proposed in this paper is applied to the 4-class classification of breast cancer histology images and achieves 95% accuracy on the initial test set and 88.89% accuracy on the overall test set. The results are competitive compared to the results of other state-of-the-art methods.