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Towards Real-World Federated Learning in Medical Image Analysis Using Kaapana

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Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health (DeCaF 2022, FAIR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13573))

Abstract

The Radiological Cooperative Network (RACOON) is dedicated to strengthening Covid-19 research by establishing a standardized digital infrastructure across all university hospitals in Germany. Using a combination of structured reporting together with advanced image analysis methods, it is possible to train new models for a standardized and automated biomarker extraction that can be easily rolled out across the consortium. A major challenge consists in providing generic and robust tools that work well on relevant data from all hospitals, not just on those where the model was originally trained. Potential solutions are federated approaches that incorporate data from all sites for model generation. In this work, we therefore extend the Kaapana framework used in RACOON to enable real-world federated learning in clinical environments. In addition, we create a benchmark of the nnU-Net when applied in multi-site settings by conducting intra- and cross-site experiments on a multi-site prostate segmentation dataset.

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Notes

  1. 1.

    https://kaapana.readthedocs.io/en/release-0.1.3/.

  2. 2.

    https://liuquande.github.io/SAML/.

  3. 3.

    https://github.com/kaapana/kaapana.

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Acknowledgements

This research was supported by the German Cancer Consortium (DKTK, Strategic Initiative Joint Imaging Platform), the Helmholtz Association within the project Trustworthy Federated Data Analytics (TFDA) (funding number ZT-I-OO1 4) and by the German Federal Ministry of Education and Research (BMBF) as part of the University Medicine Network (Project RACOON, 01KX2021). Furthermore, we thank Niklas Kühl from the Karlsruhe Service Research Institute (KSRI) and our colleagues at the German Cancer Research Center who were involved in making this work possible, especially Constantin Ulrich, Fabian Isensee, Markus Bujotzek, Maximilian Fischer, Michael Baumgartner, Peter Neher, Piermarco Pascale and Philipp Schader.

Author information

Authors and Affiliations

  1. Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Klaus Kades, Jonas Scherer, Maximilian Zenk & Klaus Maier-Hein

  2. Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany

    Klaus Kades

  3. Medical Faculty, Heidelberg University, Heidelberg, Germany

    Jonas Scherer

  4. Karlsruhe Service Research Institute (KSRI), Karlsruhe, Germany

    Marius Kempf

  5. Pattern Analysis and Learning Group, Heidelberg University Hospital, Heidelberg, Germany

    Klaus Maier-Hein

Authors
  1. Klaus Kades
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  2. Jonas Scherer
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  3. Maximilian Zenk
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  5. Klaus Maier-Hein
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Corresponding author

Correspondence to Klaus Kades .

Editor information

Editors and Affiliations

  1. University of Bonn, Bonn, Germany

    Shadi Albarqouni

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

  3. University College London, London, UK

    Sophia Bano

  4. King’s College London, London, UK

    M. Jorge Cardoso

  5. NepAl Applied Mathematics and Informatics, Kathmandu, Nepal

    Bishesh Khanal

  6. Vanderbilt University, Brentwood, TN, USA

    Bennett Landman

  7. University of British Columbia, Vancouver, BC, Canada

    Xiaoxiao Li

  8. University of Edinburgh, Edinburgh, UK

    Chen Qin

  9. Istanbul Technical University, Istanbul, Turkey

    Islem Rekik

  10. NVIDIA GmbH, Munich, Bayern, Germany

    Nicola Rieke

  11. NVIDIA Corporation, Santa Clara, CA, USA

    Holger Roth

  12. Indian Institute of Technology, Kharagpur, India

    Debdoot Sheet

  13. NVIDIA Corporation, Santa Clara, CA, USA

    Daguang Xu

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Kades, K., Scherer, J., Zenk, M., Kempf, M., Maier-Hein, K. (2022). Towards Real-World Federated Learning in Medical Image Analysis Using Kaapana. In: Albarqouni, S., et al. Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health. DeCaF FAIR 2022 2022. Lecture Notes in Computer Science, vol 13573. Springer, Cham. https://doi.org/10.1007/978-3-031-18523-6_13

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  • DOI: https://doi.org/10.1007/978-3-031-18523-6_13

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  • Online ISBN: 978-3-031-18523-6

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