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2022 | OriginalPaper | Buchkapitel

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

verfasst von : Klaus Kades, Jonas Scherer, Maximilian Zenk, Marius Kempf, Klaus Maier-Hein

Erschienen in: Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health

Verlag: Springer Nature Switzerland

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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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Vorheriges Kapitel Towards More Efficient Data Valuation in Healthcare Federated Learning Using Ensembling

Nächstes Kapitel Towards Sparsified Federated Neuroimaging Models via Weight Pruning

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

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

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Titel: Towards Real-World Federated Learning in Medical Image Analysis Using Kaapana
verfasst von: Klaus Kades
Jonas Scherer
Maximilian Zenk
Marius Kempf
Klaus Maier-Hein
Verlag: Springer Nature Switzerland
Buch: Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health
Print ISBN: 978-3-031-18522-9

Electronic ISBN: 978-3-031-18523-6

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-031-18523-6_13

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