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Sage Bionetworks

United States

1 paper here4 citations across 1
fields
  • cs.CV1
ROR 049ncjx51OpenAlex

affiliations via OpenAlex

most citedThe MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning

4 citations

researchers with a paper here
  • Akis Linardos1 · h 9
  • Bjoern H Menze1 · h 71
  • Brandon Edwards1 · h 9
  • Daewoon Kim1 · h 3
  • David Naccache1 · h 1
  • Dimitrios Makris1 · h 3
  • Elina Kontio1 · h 4
  • Giacomo Tarroni1 · h 2
  • Gustav Grimberg1 · h 2
  • I. Ezhov1 · h 25
  • Jonathan Passerat-Palmbach1 · h 3
  • J. Paetzold1 · h 24
collaborating institutions
  • City St George's, University of LondonGB1 paper
  • Cornell UniversityUS1 paper
  • Imperial College LondonGB1 paper
  • Indiana University HealthUS1 paper
  • Indiana University – Purdue University IndianapolisUS1 paper
  • Indiana University School of Medicine1 paper
  • Intel (United States)US1 paper
  • Kingston University LondonGB1 paper
  • Laboratoire de Géologie de l’École Normale SupérieureFR1 paper
  • OTH RegensburgDE1 paper
  • Seoul National UniversityKR1 paper
  • Stanford UniversityUS1 paper

1 paper

cs.CV2025★ 4 cited

The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning

Akis Linardos, Sarthak Pati, Ujjwal Baid +25

We present the design and results of the MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024, which focuses on federated learning (FL) for glioma sub-region segmentation in m…

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