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Assessment of Breast Cancer Histology Using Densely Connected Convolutional Networks

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Image Analysis and Recognition (ICIAR 2018)

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

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Abstract

Breast cancer is the most frequently diagnosed cancer and leading cause of cancer-related death among females worldwide. In this article, we investigate the applicability of densely connected convolutional neural networks to the problems of histology image classification and whole slide image segmentation in the area of computer-aided diagnoses for breast cancer. To this end, we study various approaches for transfer learning and apply them to the data set from the 2018 grand challenge on breast cancer histology images (BACH).

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Author information

Authors and Affiliations

  1. Konica Minolta Laboratory Europe, Munich, Germany

    Matthias Kohl, Florian Ludwig, Stefan Braunewell & Maximilian Baust

  2. Institute of Pathology, Faculty of Medicine, LMU Munich, Munich, Germany

    Christoph Walz

Authors
  1. Matthias Kohl
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  2. Christoph Walz
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  3. Florian Ludwig
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  4. Stefan Braunewell
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  5. Maximilian Baust
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Corresponding author

Correspondence to Maximilian Baust .

Editor information

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

    Bart ter Haar Romeny

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Kohl, M., Walz, C., Ludwig, F., Braunewell, S., Baust, M. (2018). Assessment of Breast Cancer Histology Using Densely Connected Convolutional Networks. 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_103

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  • DOI: https://doi.org/10.1007/978-3-319-93000-8_103

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92999-6

  • Online ISBN: 978-3-319-93000-8

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