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Proceedings Paper

Convolutional neural network for automated histopathological grading of breast cancer on digital mammograms
Author(s): Jinjin Hai; Hongna Tan; Lei Zeng; Minghui Wu; Kai Qiao; Jingbo Xu; Dapeng Shi; Bin Yan
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Paper 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.

Paper Details

Date Published: 9 August 2018
PDF: 8 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108065I (9 August 2018); doi: 10.1117/12.2503019
Show Author Affiliations
Jinjin Hai, National Digital Switching System Engineering and Technological Research Ctr. (China)
Hongna Tan, Henan Provincial People’s Hospital (China)
Lei Zeng, National Digital Switching System Engineering and Technological Research Ctr. (China)
Minghui Wu, Henan Provincial People’s Hospital (China)
Kai Qiao, National Digital Switching System Engineering and Technological Research Ctr. (China)
Jingbo Xu, National Digital Switching System Engineering and Technological Research Ctr. (China)
Dapeng Shi, Henan Provincial People’s Hospital (China)
Bin Yan, National Digital Switching System Engineering and Technological Research Ctr. (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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