The Federated Tumor Segmentation (FeTS) initiative, describes the on-going development of i) the largest international federation of healthcare institutions, and ii) an open-source toolkit with a user-friendly GUI, aiming at gaining knowledge for tumor boundary detection from ample and diverse patient populations without sharing any patient data.

The FeTS toolkit focuses on:

bringing pre- trained segmentation models of numerous deep learning algorithms and their fusion, closer to clinical experts and researchers, thereby enabling easy quantification of new radiographic scans and comparative evaluation of new algorithms.
allowing secure multi- institutional collaborations via federated learning to improve these pre-trained models without sharing patient data, thereby overcoming legal, privacy, and data-ownership challenges.