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Hybrid Artificial Intelligence for Knowledge Representation and Model-Based Medical Image Understanding - Towards Explainability

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Discrete Geometry and Mathematical Morphology (DGMM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13493))

Abstract

In this paper, we advocate that combining several frameworks in artificial intelligence, adopting a hybrid point of view for both knowledge data representation and reasoning, offers opportunities towards explainability. This idea is illustrated on the example of image understanding, in particular in medical imaging, formulated as a spatial reasoning problem.

This work was partly supported by the author’s chair in Artificial Intelligence (Sorbonne Université and SCAI). A part of the work mentioned in this paper was performed while the author was with LTCI, Télécom Paris, Institut Polytechnique de Paris.

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Notes

  1. 1.

    These are only examples and similar approaches have been developed in other application domains, such as satellite imaging, video, music representations, etc.

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Acknowledgements

The author would like to emphasize that the ideas summarized in this paper benefited from many joint works with post-doctoral researchers and PhD candidates, with colleagues in universities and research centers in several countries, with university hospitals, and with industrial partners. Thanks to all of them!

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Authors and Affiliations

  1. Sorbonne Université, CNRS, LIP6, Paris, France

    Isabelle Bloch

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  1. Isabelle Bloch
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Correspondence to Isabelle Bloch .

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  1. University of Strasbourg, Strasbourg, France

    Étienne Baudrier

  2. University of Strasbourg, Strasbourg, France

    Benoît Naegel

  3. University of Strasbourg, Strasbourg, France

    Adrien Krähenbühl

  4. University of Strasbourg, Strasbourg, France

    Mohamed Tajine

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Bloch, I. (2022). Hybrid Artificial Intelligence for Knowledge Representation and Model-Based Medical Image Understanding - Towards Explainability. In: Baudrier, É., Naegel, B., Krähenbühl, A., Tajine, M. (eds) Discrete Geometry and Mathematical Morphology. DGMM 2022. Lecture Notes in Computer Science, vol 13493. Springer, Cham. https://doi.org/10.1007/978-3-031-19897-7_2

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  • DOI: https://doi.org/10.1007/978-3-031-19897-7_2

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