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On Transferability of Histological Tissue Labels in Computational Pathology

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12374))

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Abstract

Deep learning tools in computational pathology, unlike natural vision tasks, face with limited histological tissue labels for classification. This is due to expensive procedure of annotation done by expert pathologist. As a result, the current models are limited to particular diagnostic task in mind where the training workflow is repeated for different organ sites and diseases. In this paper, we explore the possibility of transferring diagnostically-relevant histology labels from a source-domain into multiple target-domains to classify similar tissue structures and cancer grades. We achieve this by training a Convolutional Neural Network (CNN) model on a source-domain of diverse histological tissue labels for classification and then transfer them to different target domains for diagnosis without re-training/fine-tuning (zero-shot). We expedite this by an efficient color augmentation to account for color disparity across different tissue scans and conduct thorough experiments for evaluation.

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Notes

  1. 1.

    https://github.com/mahdihosseini/HistoLabelTransfer/.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada

    Mahdi S. Hosseini, Lyndon Chan, Weimin Huang, Yichen Wang, Danial Hasan & Konstantinos N. Plataniotis

  2. Division of Pathology, St. Michaels Hospital, Toronto, ON, M4N 1X3, Canada

    Corwyn Rowsell

  3. Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada

    Corwyn Rowsell

  4. Huron Digital Pathology, St. Jacobs, ON, N0B 2N0, Canada

    Savvas Damaskinos

Authors
  1. Mahdi S. Hosseini
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  2. Lyndon Chan
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  3. Weimin Huang
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  4. Yichen Wang
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  5. Danial Hasan
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  6. Corwyn Rowsell
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  7. Savvas Damaskinos
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  8. Konstantinos N. Plataniotis
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Corresponding author

Correspondence to Mahdi S. Hosseini .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Hosseini, M.S. et al. (2020). On Transferability of Histological Tissue Labels in Computational Pathology. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12374. Springer, Cham. https://doi.org/10.1007/978-3-030-58526-6_27

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  • DOI: https://doi.org/10.1007/978-3-030-58526-6_27

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