Convolutional neural network for automated histopathological grading of breast cancer on digital mammograms

Jinjin Hai; Hongna Tan; Lei Zeng; Minghui Wu; Kai Qiao; Jingbo Xu; Dapeng Shi; Bin Yan

doi:10.1117/12.2503019

9 August 2018 Convolutional neural network for automated histopathological grading of breast cancer on digital mammograms

Jinjin Hai, Hongna Tan, Lei Zeng, Minghui Wu, Kai Qiao, Jingbo Xu, Dapeng Shi, Bin Yan

Author Affiliations +

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108065I (2018) https://doi.org/10.1117/12.2503019
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

Abstract

Histopathological grading of breast cancer is an important tumor-related prognostic factor and plays an important role in breast cancer prognosis analysis. Nowadays, histopathological grading of breast cancer is mainly identified by pathological images and radiologists cannot differentiate the histopathological grade directly from digital mammograms. In this paper, we propose to discriminate the histopathological grades directly based on digital mammograms, which is noninvasive and convenient. End-to-end training Convolutional Neural Network (CNN) is firstly designed to extract semantic features directly from raw image data. Considering the scarce annotated mammograms data and large size of tumor region, a light and deep network with less training parameters is modified to prevent overfitting. Results demonstrate that our proposed network is superior to other CNN models and traditional classifier based on hand-crafted features.

Citation Download Citation

Jinjin Hai, Hongna Tan, Lei Zeng, Minghui Wu, Kai Qiao, Jingbo Xu, Dapeng Shi, and Bin Yan "Convolutional neural network for automated histopathological grading of breast cancer on digital mammograms", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108065I (9 August 2018); https://doi.org/10.1117/12.2503019

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