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Evolutionary design of explainable algorithms for biomedical image segmentation

Author

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  • Kévin Cortacero

    (Centre de Recherche en Cancérologie de Toulouse (CRCT)
    Centre National de la Recherche Scientifique (CNRS) UMR5071
    University of Toulouse III - Paul Sabatier)

  • Brienne McKenzie

    (Centre de Recherche en Cancérologie de Toulouse (CRCT)
    Centre National de la Recherche Scientifique (CNRS) UMR5071
    University of Toulouse III - Paul Sabatier)

  • Sabina Müller

    (Centre de Recherche en Cancérologie de Toulouse (CRCT)
    Centre National de la Recherche Scientifique (CNRS) UMR5071
    University of Toulouse III - Paul Sabatier)

  • Roxana Khazen

    (Centre de Recherche en Cancérologie de Toulouse (CRCT)
    Centre National de la Recherche Scientifique (CNRS) UMR5071
    University of Toulouse III - Paul Sabatier)

  • Fanny Lafouresse

    (Centre de Recherche en Cancérologie de Toulouse (CRCT)
    Centre National de la Recherche Scientifique (CNRS) UMR5071
    University of Toulouse III - Paul Sabatier)

  • Gaëlle Corsaut

    (Centre de Recherche en Cancérologie de Toulouse (CRCT)
    Centre National de la Recherche Scientifique (CNRS) UMR5071
    University of Toulouse III - Paul Sabatier)

  • Nathalie Acker

    (Institut Universitaire du Cancer-Oncopole de Toulouse (IUCT))

  • François-Xavier Frenois

    (Institut Universitaire du Cancer-Oncopole de Toulouse (IUCT))

  • Laurence Lamant

    (Institut Universitaire du Cancer-Oncopole de Toulouse (IUCT))

  • Nicolas Meyer

    (IUCT)

  • Béatrice Vergier

    (Centre Hospitalier Universitaire de Bordeaux
    Université de Bordeaux)

  • Dennis G. Wilson

    (Artificial and Natural Intelligence Toulouse Institute)

  • Hervé Luga

    (Artificial and Natural Intelligence Toulouse Institute)

  • Oskar Staufer

    (University of Oxford
    Leibniz Institute for New Materials)

  • Michael L. Dustin

    (University of Oxford)

  • Salvatore Valitutti

    (Centre de Recherche en Cancérologie de Toulouse (CRCT)
    Centre National de la Recherche Scientifique (CNRS) UMR5071
    University of Toulouse III - Paul Sabatier
    Institut Universitaire du Cancer-Oncopole de Toulouse (IUCT))

  • Sylvain Cussat-Blanc

    (Artificial and Natural Intelligence Toulouse Institute)

Abstract

An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting “black box” models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets. This Few-Shot Learning method confers tremendous flexibility, speed, and functionality to this approach. We then deploy Kartezio to solve a series of semantic and instance segmentation problems, and demonstrate its utility across diverse images ranging from multiplexed tissue histopathology images to high resolution microscopy images. While the flexibility, robustness and practical utility of Kartezio make this fully explicable evolutionary designer a potential game-changer in the field of biomedical image processing, Kartezio remains complementary and potentially auxiliary to mainstream Deep Learning approaches.

Suggested Citation

  • Kévin Cortacero & Brienne McKenzie & Sabina Müller & Roxana Khazen & Fanny Lafouresse & Gaëlle Corsaut & Nathalie Acker & François-Xavier Frenois & Laurence Lamant & Nicolas Meyer & Béatrice Vergier &, 2023. "Evolutionary design of explainable algorithms for biomedical image segmentation," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42664-x
    DOI: 10.1038/s41467-023-42664-x
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    References listed on IDEAS

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