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Update README.md #902

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Expand Up @@ -67,12 +67,11 @@ OpenFL supports training with TensorFlow 2+ or PyTorch 1.3+ which should be inst


### Background
OpenFL builds on a collaboration between Intel and the University of Pennsylvania (UPenn) to develop the [Federated Tumor Segmentation (FeTS, www.fets.ai)](https://www.fets.ai/) platform (grant award number: U01-CA242871).
OpenFL builds on a collaboration between Intel and the Bakas lab at the University of Pennsylvania (UPenn) to develop the [Federated Tumor Segmentation (FeTS, www.fets.ai)](https://www.fets.ai/) platform (grant award number: U01-CA242871).

The grant for FeTS was awarded to the [Center for Biomedical Image Computing and Analytics (CBICA)](https://www.cbica.upenn.edu/) at UPenn (PI: S. Bakas) from the [Informatics Technology for Cancer Research (ITCR)](https://itcr.cancer.gov/) program of the National Cancer Institute (NCI) of the National Institutes of Health (NIH).
The grant for FeTS was awarded from the [Informatics Technology for Cancer Research (ITCR)](https://itcr.cancer.gov/) program of the National Cancer Institute (NCI) of the National Institutes of Health (NIH), to Dr Spyridon Bakas (Principal Investigator) when he was affiliated with the [Center for Biomedical Image Computing and Analytics (CBICA)](https://www.cbica.upenn.edu/) at UPenn and now heading up the [Division of Computational Pathology at Indiana University (IU)](https://medicine.iu.edu/pathology/research/computational-pathology).

FeTS is a real-world medical federated learning platform with international collaborators. The original OpenFederatedLearning project and OpenFL are designed to serve as the backend for the FeTS platform,
and OpenFL developers and researchers continue to work very closely with UPenn on the FeTS project. An example is the [FeTS-AI/Front-End](https://github.com/FETS-AI/Front-End), which integrates UPenn’s medical AI expertise with OpenFL framework to create a federated learning solution for medical imaging.
FeTS is a real-world medical federated learning platform with international collaborators. The original OpenFederatedLearning project and OpenFL are designed to serve as the backend for the FeTS platform, and OpenFL developers and researchers continue to work very closely with IU on the FeTS project. An example is the [FeTS-AI/Front-End](https://github.com/FETS-AI/Front-End), which integrates the group’s medical AI expertise with OpenFL framework to create a federated learning solution for medical imaging.

Although initially developed for use in medical imaging, OpenFL designed to be agnostic to the use-case, the industry, and the machine learning framework.

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