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Abstract: Learned Image Compression for HE-stained Histopathological Images via Stain Deconvolution

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Bildverarbeitung für die Medizin 2025 (BVM 2025)

Part of the book series: Informatik aktuell ((INFORMAT))

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Abstract

Processing histopathological whole slide images (WSI) leads to massive storage requirements for clinics worldwide. Even after lossy image compression during image acquisition, additional lossy compression is frequently possible without substantially affecting the performance of deep learning-based (DL) downstream tasks. In this paper, we show that the commonly used JPEG algorithm is not best suited for further compression and we propose stain quantized latent compression (SQLC), a novel DL based histopathology data compression approach.

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References

  1. Fischer M, Neher P,Wald T et al. Learned image compression for HE-stained histopathological images via stain deconvolution. Proc MICCAI. 2024.

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Authors and Affiliations

  1. German Cancer Research Center, Heidelberg, Deutschland

    Maximilian Fischer, Peter Neher, Tassilo Wald, Constantin Ulrich, Shuhan Xiao, Silvia Dias Almeida, Michael Götz, Marco Nolden & Klaus Maier-Hein

  2. Technical University of Munich, München, Deutschland

    Peter Schüffler, Alexander Muckenhuber & Rickmer Braren

  3. University Medicine Essen, Essen, Deutschland

    Jens Kleesiek

Authors
  1. Maximilian Fischer
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  2. Peter Neher
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  3. Tassilo Wald
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  4. Constantin Ulrich
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  5. Peter Schüffler
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  6. Shuhan Xiao
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  7. Silvia Dias Almeida
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  8. Alexander Muckenhuber
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  9. Rickmer Braren
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  10. Michael Götz
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  11. Jens Kleesiek
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  12. Marco Nolden
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  13. Klaus Maier-Hein
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Corresponding author

Correspondence to Maximilian Fischer .

Editor information

Editors and Affiliations

  1. Fakultät für Informatik und Mathematik, Ostbayerische Technische Hochschule Regensburg, Regensburg, Deutschland

    Christoph Palm

  2. Center for Artificial Intelligence and Data Science (CAIDAS), Universität Würzburg, Würzburg, Deutschland

    Katharina Breininger

  3. Peter L. Reichertz, Institut für Medizinische Informatik, Technische Universität Braunschweig und Medizinische Hochschule Hannover, Braunschweig, Niedersachsen, Deutschland

    Thomas Deserno

  4. Institut für Medizinische Informatik, Universität zu Lübeck, Lübeck, Schleswig-Holstein, Deutschland

    Heinz Handels

  5. Lehrstuhl für Mustererkennung, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Bayern, Deutschland

    Andreas Maier

  6. Medical Image Computing E230, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Baden-Württemberg, Deutschland

    Klaus H. Maier-Hein

  7. Institut für Medizinische Informatik, Charité - Universitätsmedizin Berlin, Berlin, Berlin, Deutschland

    Thomas M. Tolxdorff

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© 2025 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

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Fischer, M. et al. (2025). Abstract: Learned Image Compression for HE-stained Histopathological Images via Stain Deconvolution. In: Palm, C., et al. Bildverarbeitung für die Medizin 2025. BVM 2025. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-47422-5_79

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