期刊
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
卷 71, 期 -, 页码 58-66出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compmedimag.2018.10.008
关键词
Breast cancer pathological grading; Digital mammograms; LASSO logistic regression; Convolutional Neural Network; Multi-level features
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. (C) 2018 Published by Elsevier Ltd.
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