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A Deep Residual Inception Network for HEp-2 Cell Classification

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Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (DLMIA 2017, ML-CDS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10553))

Abstract

Indirect-immunofluorescence (IIF) of Human Epithelial-2 (HEp-2) cells is a commonly-used method for the diagnosis of autoimmune diseases. Traditional approach relies on specialists to observe HEp-2 slides via the fluorescence microscope, which suffers from a number of shortcomings like being subjective and labor intensive. In this paper, we proposed a hybrid deep learning network combining the latest high-performance network architectures, i.e. ResNet and Inception, to automatically classify HEp-2 cell images. The proposed Deep Residual Inception (DRI) net replaces the plain convolutional layers in Inception with residual modules for better network optimization and fuses the features extracted from shallow, medium and deep layers for performance improvement. The proposed model is evaluated on publicly available I3A (Indirect Immunofluorescence Image Analysis) dataset. The experiment results demonstrate that our proposed DRI remarkably outperforms the benchmarking approaches.

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Notes

  1. 1.

    http://nerone.diem.unisa.it/hep2-benchmarking/dbtools/.

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Author information

Authors and Affiliations

  1. Computer Vision Institute, Shenzhen University, Shenzhen, China

    Yuexiang Li & Linlin Shen

Authors
  1. Yuexiang Li
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  2. Linlin Shen
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Corresponding author

Correspondence to Linlin Shen .

Editor information

Editors and Affiliations

  1. University College London, London, United Kingdom

    M. Jorge Cardoso

  2. McGill University, Montreal, Québec, Canada

    Tal Arbel

  3. University of Adelaide, Adelaide, South Australia, Australia

    Gustavo Carneiro

  4. IBM Research - Almaden, San Jose, California, USA

    Tanveer Syeda-Mahmood

  5. Universidade do Porto, Porto, Portugal

    João Manuel R.S. Tavares

  6. IBM Research - Almaden, San Jose, USA

    Mehdi Moradi

  7. University of Queensland, Brisbane, Queensland, Australia

    Andrew Bradley

  8. Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  9. Universidade Estadual Paulista, Bauru, Brazil

    João Paulo Papa

  10. Case Western Reserve University, Cleveland, Ohio, USA

    Anant Madabhushi

  11. Instituto Superior Técnico, Lisboa, Portugal

    Jacinto C. Nascimento

  12. Universidade do Porto, Porto, Portugal

    Jaime S. Cardoso

  13. University of Oxford, Oxford, United Kingdom

    Vasileios Belagiannis

  14. University of South Australia, Adelaide, South Australia, Australia

    Zhi Lu

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Li, Y., Shen, L. (2017). A Deep Residual Inception Network for HEp-2 Cell Classification. In: Cardoso, M., et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham. https://doi.org/10.1007/978-3-319-67558-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-67558-9_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67557-2

  • Online ISBN: 978-3-319-67558-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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