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Breast Cancer Histological Image Classification Using Fine-Tuned Deep Network Fusion

  • Amirreza MahbodEmail author
  • Isabella Ellinger
  • Rupert Ecker
  • Örjan Smedby
  • Chunliang Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10882)

Abstract

Breast cancer is the most common cancer type in women worldwide. Histological evaluation of the breast biopsies is a challenging task even for experienced pathologists. In this paper, we propose a fully automatic method to classify breast cancer histological images to four classes, namely normal, benign, in situ carcinoma and invasive carcinoma. The proposed method takes normalized hematoxylin and eosin stained images as input and gives the final prediction by fusing the output of two residual neural networks (ResNet) of different depth. These ResNets were first pre-trained on ImageNet images, and then fine-tuned on breast histological images. We found that our approach outperformed a previous published method by a large margin when applied on the BioImaging 2015 challenge dataset yielding an accuracy of 97.22%. Moreover, the same approach provided an excellent classification performance with an accuracy of 88.50% when applied on the ICIAR 2018 grand challenge dataset using 5-fold cross validation.

Keywords

Breast cancer Histological images Classification Deep learning 

Notes

Acknowledgments

This project is supported by Horizon 2020 Framework of the European Union in the CaSR Biomedicine project, No. 675228.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Amirreza Mahbod
    • 1
    • 2
    Email author
  • Isabella Ellinger
    • 1
  • Rupert Ecker
    • 2
  • Örjan Smedby
    • 3
  • Chunliang Wang
    • 3
  1. 1.Institute of Pathophysiology and Allergy ResearchMedical University of ViennaViennaAustria
  2. 2.Department of Research and DevelopmentTissueGnostics GmbHViennaAustria
  3. 3.Department of Biomedical Engineering and Health Systems, Division of Biomedical ImagingKTH Royal Institute of TechnologyStockholmSweden

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