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Multi-task deep learning for fine-grained classification and grading in breast cancer histopathological images

  • Lingqiao Li
  • Xipeng Pan
  • Huihua Yang
  • Zhenbing Liu
  • Yubei He
  • Zhongming Li
  • Yongxian Fan
  • Zhiwei Cao
  • Longhao Zhang
Article
  • 26 Downloads

Abstract

Fine-grained classification and grading of breast cancer (BC) histopathological images are of great value in clinical application. However, automatic classification and grading of BC histopathological images are complicated by (1) small inter-class variance and large intra-class variance exist in BC histopathological images, and (2) features extracted from similar histopathological images with different magnification are quite different. To address these issues, an improved deep convolution neural network model is proposed and the procedure can be divided into three main stages. Firstly, in the representation learning process, multi-class recognition task and verification task of image pair are combined. Secondly, in the feature extraction process, a prior knowledge is built, which is “the variances in feature outputs between different subclasses is relatively large while the variance between the same subclass is small.” Additionally, the prior information that histopathological images with different magnification belong to the same subclass are embedded in the feature extraction process, which contributes to less sensitive with image magnification. The experimental results based on three different histopathological image datasets show that the performance of the proposed method is better than state of the art, with better 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. [33], Dimitropoulos et al. [8], and Dr. Andrew Janowczyk et al. [16] for publishing the datasets. We would like to express our gratitude to anonymous reviewers and editor for their 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). The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  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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