Multi-level features combined end-to-end learning for automated pathological grading of breast cancer on digital mammograms.

We propose to discriminate the pathological grades directly on digital mammograms instead of pathological images. An end-to-end learning algorithm based on the combined multi-level features is proposed. Low-level features are extracted and selected by supervised LASSO logistic regression. Convolutional Neural Network (CNN) is designed to extract high-level semantic features. These extracted multi-level features are combined to optimize the new CNN end to end to make different parts of the network learn to pay attention to different level of features. Results demonstrate that our proposed algorithm is superior to other CNN models and obtain comparable performance compared with pathological images.