Federated Tumor Segmentation Challenge

Repo for FeTS Challenge: The 1st Computational Competition on Federated Learning. Year 2024 and beyond

Website

https://www.synapse.org/#!Synapse:syn28546456

Challenge Task

The challenge involves customizing core functions of a baseline federated learning system implementation. The goal is to improve over the baseline consensus models in terms of robustness in final model scores to data heterogeneity across the simulated collaborators of the federation. For more details, please see Task_1.

Documentation and Q&A

Please visit the challenge website and forum.

Citation

Please cite this paper when using the data:

@misc{pati2021federated,
      title={The Federated Tumor Segmentation (FeTS) Challenge}, 
      author={Sarthak Pati and Ujjwal Baid and Maximilian Zenk and Brandon Edwards and Micah Sheller and G. Anthony Reina and Patrick Foley and Alexey Gruzdev and Jason Martin and Shadi Albarqouni and Yong Chen and Russell Taki Shinohara and Annika Reinke and David Zimmerer and John B. Freymann and Justin S. Kirby and Christos Davatzikos and Rivka R. Colen and Aikaterini Kotrotsou and Daniel Marcus and Mikhail Milchenko and Arash Nazer and Hassan Fathallah-Shaykh and Roland Wiest and Andras Jakab and Marc-Andre Weber and Abhishek Mahajan and Lena Maier-Hein and Jens Kleesiek and Bjoern Menze and Klaus Maier-Hein and Spyridon Bakas},
      year={2021},
      eprint={2105.05874},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}