Advanced search

Filter By:

Journal Check one or more journals to show results from those journals only.

Choose more journals

Article type Check one or more article types to show results from those article types only.
Subject Check one or more subjects to show results from those subjects only.
Date Choose a date option to show results from those dates only.

Custom date range

Clear all filters
Sort by:
Showing 1–4 of 4 results
Advanced filters: Author: Alexandre Xavier Falcão 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

  • Here, the authors present results of the ZiBRA-2 project (https://www.zibra2project.org) which is an arbovirus surveillance project, across the Midwest of Brazil using a mobile genomics laboratory, combined with a genomic surveillance training program that targeted post-graduate students, laboratory technicians, and health practitioners in universities and laboratories.

    • Talita Émile Ribeiro Adelino
    • Marta Giovanetti
    • Luiz Carlos Junior Alcantara
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-12