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Deep Learning for Histopathological Image Analysis

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Deep Learning for Biomedical Data Analysis

Abstract

Anatomical Pathology dates back to the nineteenth century when Rudolf Virchow introduced his concept of cellular pathology and when the technical improvements of light microscopy enabled wide-spread use of structural criteria to define diseases. Since then, the quality of optical instruments has been constantly evolving. However the central element of the diagnostic process remains the knowledge and experience of pathologists visually classifying observations according to internationally agreed guidelines (e.g., World Health Organisation (WHO) classification), and much of the pre-analytical steps of specimen preparation (e.g., fixation, embedding, sectioning, staining) is only partially automated and still requires many manual steps. Thanks to the recent advent and cost-effectiveness of digital scanners, tissue histopathology slides can now be fully digitized and stored as Whole Slide Images (WSI). With the availability and analysis of a much larger set of variables combined with sophisticated imaging and analytic techniques, the traditional paradigm of pathology based on visually descriptive microscopy can be complemented and substantially improved by digital pathology, utilizing screen-based visualization of digital tissue sections and novel analysis tools potentially combining the conventional evaluation by pathologists with a computer-based diagnostic aid system. A central element of such evolving medical utilities and decision support systems will be image analysis, a field in which Deep Learning (DL) has recently made immense progress and especially the development of very large Artificial Neural Networks (ANNs) that are revolutionizing the field. Indeed, they have surpassed all existing image processing methods in most fields (segmentation, object detection, classification, etc.). All current methods applied to histopathological image analysis will be presented as well as the future technological issues and challenges of this discipline.

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  1. 1.

    https://peterjacknaylor.github.io/.

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Acknowledgements

This work was performed in the framework of SYSIMIT (FKZ:01ZX1308A) funded by German Ministry for Education and Research (BMBF) and the ERACoSysMed project SysMIFTA under the grant agreement No. 031L0085 (BMBF, Projektträger Jülich, Germany) and No. ANR-15-CMED-0004-03 (ANR, France). This work was also supported under the framework of the IdEx Unistra and benefits from a funding from the state managed by the French National Research Agency as part of the Investments for the future program.

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Authors and Affiliations

  1. ICube, University of Strasbourg, Strasbourg, France

    Cédric Wemmert

  2. IRIMAS, University of Haute-Alsace, Mulhouse, France

    Jonathan Weber & Germain Forestier

  3. Hannover Medical School, Hannover, Germany

    Friedrich Feuerhake

  4. University Clinic of Freiburg, Freiburg, Germany

    Friedrich Feuerhake

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    Mourad Elloumi

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Wemmert, C., Weber, J., Feuerhake, F., Forestier, G. (2021). Deep Learning for Histopathological Image Analysis. In: Elloumi, M. (eds) Deep Learning for Biomedical Data Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-71676-9_7

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