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

  • Xipeng Pan
  • Lingqiao Li
  • Huihua YangEmail author
  • Zhenbing LiuEmail author
  • Yubei He
  • Zhongming Li
  • Yongxian Fan
  • Zhiwei Cao
  • Longhao Zhang
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 810)

Abstract

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.

Keywords

Multi-task deep learning Histopathological image classification Fine-grained Convolutional neural network Breast cancer 

Notes

Acknowledgements

The authors would like to thank Spanhol et al. [8], and Dimitropoulos et al. [3] for publishing the datasets. We would like to express our gratitude to anonymous reviewers and editor for helpful comments. This research was supported in part by the National Natural Science Foundation of China (Grant Nos. 21365008, 61462018, 61762026 and 61562013), and Natural Science Foundation of Guangxi Province (No. 2017GXNSFDA198025 and 2017GXNSFAA198278).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Xipeng Pan
    • 1
  • Lingqiao Li
    • 1
    • 2
  • Huihua Yang
    • 1
    • 2
    Email author
  • Zhenbing Liu
    • 2
    Email author
  • Yubei He
    • 3
  • Zhongming Li
    • 1
  • Yongxian Fan
    • 2
  • Zhiwei Cao
    • 1
  • Longhao Zhang
    • 1
  1. 1.School of AutomationBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.School of Computer Science and Information SecurityGuilin University of Electronic TechnologyGuilinChina
  3. 3.School of Computing and Information SystemsUniversity of MelbourneMelbourneAustralia

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