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 https://www.fets.ai/
Status & Timeline
- FeTS is currently undergoing a Phase-1 evaluation with a limited number of international collaborating institutions.
- The Phase-1 evaluation is expected to go on until the end of Q4 2020.
- Phase-2 evaluation, including all committed international collaborators is expected to follow the end of Phase-1.
Supporting Grant
This work is in part supported by the National Institutes of Health / National Cancer Institute / Informatics Technology for Cancer Research (NIH/NCI/ITCR), under grant award number U01-CA242871.
Documentation
https://fets-ai.github.io/Front-End/
Disclaimer
- The software has been designed for research purposes only and has neither been reviewed nor approved for clinical use by the Food and Drug Administration (FDA) or by any other federal/state agency.
- Certain part of the code for this user interface (excluding dependent libraries) is governed by the license provided in https://www.med.upenn.edu/sbia/software-agreement.html unless otherwise specified.
For more details, please visit us at https://www.fets.ai/
For issues, please visit https://github.com/FETS-AI/Front-End/issues
Downloads
By downloading FeTS, you agree to our License.
Contact
For more information, please contact CBICA Software.
GitHub Distribution
We currently provide only our tagged versions of the code via GitHub. Check the "tags" using your favorite Git client after cloning our repository. The analogous commands are as follows:
git clone https://github.com/FETS-AI/Front-End.git
latesttag=$(git describe --tags)
echo checking out ${latesttag}
git checkout ${latesttag}