The Federated Tumor Segmentation (FeTS) Challenge
This manuscript describes the first challenge on Federated Learning, namely the Federated Tumor Segmentation (FeTS) challenge 2021. International challenges have become the standard for validation of biomedical image analysis methods. However, the actual performance of participating (even the winnin...
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Zusammenfassung: | This manuscript describes the first challenge on Federated Learning, namely
the Federated Tumor Segmentation (FeTS) challenge 2021. International
challenges have become the standard for validation of biomedical image analysis
methods. However, the actual performance of participating (even the winning)
algorithms on "real-world" clinical data often remains unclear, as the data
included in challenges are usually acquired in very controlled settings at few
institutions. The seemingly obvious solution of just collecting increasingly
more data from more institutions in such challenges does not scale well due to
privacy and ownership hurdles. Towards alleviating these concerns, we are
proposing the FeTS challenge 2021 to cater towards both the development and the
evaluation of models for the segmentation of intrinsically heterogeneous (in
appearance, shape, and histology) brain tumors, namely gliomas. Specifically,
the FeTS 2021 challenge uses clinically acquired, multi-institutional magnetic
resonance imaging (MRI) scans from the BraTS 2020 challenge, as well as from
various remote independent institutions included in the collaborative network
of a real-world federation (https://www.fets.ai/). The goals of the FeTS
challenge are directly represented by the two included tasks: 1) the
identification of the optimal weight aggregation approach towards the training
of a consensus model that has gained knowledge via federated learning from
multiple geographically distinct institutions, while their data are always
retained within each institution, and 2) the federated evaluation of the
generalizability of brain tumor segmentation models "in the wild", i.e. on data
from institutional distributions that were not part of the training datasets. |
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DOI: | 10.48550/arxiv.2105.05874 |