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Showing 1–4 of 4 results
Advanced filters: Author: Maximilian Zenk Clear advanced filters

  • Federated learning (FL) algorithms have emerged as a promising solution to train models for healthcare imaging across institutions while preserving privacy. Here, the authors describe the Federated Tumor Segmentation (FeTS) challenge for the decentralised benchmarking of FL algorithms and evaluation of Healthcare AI algorithm generalizability in real-world cancer imaging datasets.

    • Maximilian Zenk
    • Ujjwal Baid
    • Spyridon Bakas
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-20

  • Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here, the authors present the largest FL study to-date to generate an automatic tumor boundary detector for glioblastoma.

    • Sarthak Pati
    • Ujjwal Baid
    • Spyridon Bakas
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-17

  • Federated learning can be used to train medical AI models on sensitive personal data while preserving important privacy properties; however, the sensitive nature of the data makes it difficult to evaluate approaches reproducibly on real data. The MedPerf project presented by Karargyris et al. provides the tools and infrastructure to distribute models to healthcare facilities, such that they can be trained and evaluated in realistic settings.

    • Alexandros Karargyris
    • Renato Umeton
    • Peter Mattson
    ResearchOpen Access
    Nature Machine Intelligence
    Volume: 5, P: 799-810