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International Conference Image Analysis and Recognition

ICIAR 2018: Image Analysis and Recognition pp 931–940Cite as

Multiclass Classification of Breast Cancer in Whole-Slide Images

Part of the Lecture Notes in Computer Science book series (LNIP,volume 10882)

Abstract

Breast cancer is one of the leading cause of cancer-related death worldwide. During the diagnosis of breast cancer, the histopathological assessment of Haemotoxylin and Eosin(H&E) stained slides provides important clinical values. By applying computer-aid diagnosis on whole-slide image(WSI), the efficiency and consistency of such assessment could be improved. In this paper, we propose a deep learning-based framework that classifies H&E stained WSIs into regions of normal tissue, benign lesion, in-situ carcinoma and invasive carcinoma. The framework utilizes both microscopy images and WSIs to train a patch classifier in two stages. The underlying classifier is based on Inception-Resnet-v2. This framework won both parts of the ICIAR2018 Grand Challenge on Breast Cancer Histology Images [4] competition, achieved a part A multiclass accuracy of 87% and part B score of 0.6929.

Keywords

  • Breast cancer
  • Deep learning
  • Whole-Slide Images
  • Multiclass classification

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Acknowledgements

We would like to thank the organizers of ICIAR2018 and BACH2018 who supported and organized this challenge.

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Authors and Affiliations

  1. Seek AI Limited, Hong Kong, China

    Scotty Kwok

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  1. Scotty Kwok
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Correspondence to Scotty Kwok .

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Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

  3. Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Prof. Bart ter Haar Romeny

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Kwok, S. (2018). Multiclass Classification of Breast Cancer in Whole-Slide Images. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_106

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  • DOI: https://doi.org/10.1007/978-3-319-93000-8_106

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