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Multi-task Deep Learning for Fine-Grained Classification/Grading in Breast Cancer Histopathological Images

  • 作者:
    Pan, X., Li, L., Yang, H., Liu, Z., He, Y., Li, Z., Fan, Y., Cao, Z., Zhang, L.;
  • 地址:
    School of Automation, Beijing University of Posts and Telecommunications, Beijing, China[1];School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China[2];School of Computing and Information Systems, University of Melbourne, Melbourne, Australia[3];
  • 语种:
    英文
  • 期刊:
    Studies in Computational Intelligence ISSN:1860-949X 2020 年 810 卷 (85 - 95)
  • 摘要:

    The fine-grained classification or grading of breast cancer pathological images is of great value in clinical application. However, the manual feature extraction methods not only require professional knowledge, but also the cost of feature extraction is high, especially the high quality features. In this paper, we devise an improved deep convolution neural network model to achieve accurate fine-grained classification or grading of breast cancer pathological images. Meanwhile, we use online data augmentation and transfer learning strategy to avoid model overfitting. According to the issue that small inter-class variance and large intra-class variance exist in breast cancer pathological images, multi-class recognition task and verification task of image pair are combined in the representation learning process; in addition, the prior knowledge (different subclasses with relatively large distance and small distance between the same subclass) are embedded in the process of feature extraction. At the same time, the prior information that pathological images with different magnification belong to the same subclass will be embedded in the feature extraction process, which will lead to less sensitive with image magnification. Experimental results on two different pathological image datasets show that the performance of our method is better than that of state-of-the-arts, with good robustness and generalization ability. ? 2020, Springer Nature Switzerland AG.

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  • 推荐引用方式
    GB/T 7714:
    Pan X. Li L. Yang H. Liu Z. He Y. Li Z. Fan Y. Cao Z. Zhang L., et al. Multi-task Deep Learning for Fine-Grained Classification/Grading in Breast Cancer Histopathological Images [J].Studies in Computational Intelligence,2020,810:85-95.
  • APA:
    Pan X. Li L. Yang H. Liu Z. He Y. Li Z. Fan Y. Cao Z. Zhang L..(2020).Multi-task Deep Learning for Fine-Grained Classification/Grading in Breast Cancer Histopathological Images .Studies in Computational Intelligence,810:85-95.
  • MLA:
    Pan X. Li L. Yang H. Liu Z. He Y. Li Z. Fan Y. Cao Z. Zhang L., et al. "Multi-task Deep Learning for Fine-Grained Classification/Grading in Breast Cancer Histopathological Images" .Studies in Computational Intelligence 810(2020):85-95.
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