FeTS: Federated Tumor Segmentation

The Federated Tumor Segmentation (FeTS) platform, describes an on-going under development open-source toolkit, with a user-friendly graphical user interface (GUI), aiming at:

• 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.

Successful completion of this project will lead to an easy-to-use potentially-translatable tool enabling easy, fast, objective, repeatable and accurate tumor segmentation, without requiring a computational background by the user, and while facilitating further analysis of tumor radio-phenotypes towards accelerating discovery.

FeTS is developed and maintained by the Center for Biomedical Image Computing and Analytics (CBICA) at the University of Pennsylvania, in collaboration with Intel Labs, Intel AI, and Intel Internet of Things Group.

For more details, please visit us at www.fets.ai.

Table of Contents

• Application Setup
• Process the Data
• Run the Application
• Extras