An automatic breast cancer grading method in histopathological images based on pixel-, object-, and semantic-level features

We present an automatic breast cancer grading method in histopathological images based on the computer extracted pixel-, object-, and semantic-level features derived from convolutional neural networks (CNN). The multiple level features allow not only characterization of nuclear polymorphism, but also extraction of structural and interpretable information within the images. In this study, a hybrid level set based segmentation method was used to segment nuclei from the images. A quantile normalization approach was utilized to improve image color consistency. The semantic level features are extracted by a CNN approach, which describe the proportions of nuclei belonging to the different grades, in conjunction with pixel-level (texture) and object-level (structure) features, to form an integrated set of attributes. A support vector machine classifier was trained to discriminate the breast cancer between low, intermediate, and high grades. The results demonstrated that our method achieved accuracy of 0.90 (low vs. high), and 0.74 (low vs. intermediate), and 0.76 (intermediate vs. high), suggesting that the present method could play a fundamental role in developing a computer-aided breast cancer grading system.