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Breast Cancer Histological Image Classification Using Fine-Tuned Deep Network Fusion
Romania.
Romania.
Romania.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.ORCID iD: 0000-0002-7750-1917
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2018 (English)In: 15th International Conference on Image Analysis and Recognition, ICIAR 2018, Springer, 2018, p. 754-762Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2018. p. 754-762
Keywords [en]
Breast cancer, Classification, Deep learning, Histological images, Biopsy, Classification (of information), Diseases, Image analysis, Medical imaging, Automatic method, Breast biopsies, Classification performance, Cross validation, Grand Challenge, Histological evaluation, Image classification
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-236392DOI: 10.1007/978-3-319-93000-8_85Scopus ID: 2-s2.0-85049429428ISBN: 9783319929996 (print)OAI: oai:DiVA.org:kth-236392DiVA, id: diva2:1260206
Conference
27 June 2018 through 29 June 2018
Note

QC 20181101

Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2018-11-01Bibliographically approved

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Smedby, ÖrjanWang, Chunliang

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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