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README.txt
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README.txt
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/**
\mainpage Overview
The Federated Tumor Segmentation (FeTS) platform, describes an on-going under development open-source toolkit, which with a user-friendly GUI aims 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 - https://www.cbica.upenn.edu) at the University of Pennsylvania, and draws upon research from several groups within the Center and beyond.
## Bug Tracker and Feature Request
We coordinate our bugs and feature requests via out GitHub page: https://github.com/CBICA/FeTS/issues
## Frequently Asked Questions (FAQ)
Please see our [FAQ Section](gs_FAQ.html).
## Supporting Grant
This work is supported by the NIH/NCI/ITCR* grant U24-CA189523.
<br>* National Institutes of Health / National Cancer Institute / Informatics Technology for Cancer Research
## 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.
- This code (excluding dependent libraries) is governed by the license provided in https://www.med.upenn.edu/sbia/software-agreement.html unless otherwise specified.
- The minimum recommended resolution is 1024x768. We have seen some issues with high DPI screens and bug reports related to it will be appreciated.
## Contact
For more information, please contact <a href="mailto:software@cbica.upenn.edu">software@cbica.upenn.edu</a>.
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