This is the documentation for the Federated Learning Study conducted for pre-operative gliomas.
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| Application Setup |
| Process the Data |
| Run the Application |
| Extras |
| ITCR Connectivity |
This section includes a reference of all ongoing and existing connections between FeTS and other projects funded under the Informatics Technology for Cancer Research (ITCR) program.
A connectivity map featuring all ITCR projects can be found here.
FeTS uses DCMTK - DICOM ToolKit (DCMTK) for DICOM file handling.
FeTS leverages DICOM for Quantitative Imaging (DCMQI) for generating DICOM-Seg files from NIfTI files.
FeTS leverages the Cancer Imaging Phenomics Toolkit (CaPTk) for integrating CaPTk’s current functionality into FeTS.
FeTS’ performance evaluation metrics are used by Synapse PACS.
Enriching The Cancer Imaging Archive (TCIA) and Imaging Data Commons (IDC) data collections with segmentations and radiomic features. Robustness analysis on radiomic features on TCIA data has also been posted back to TCIA.
Use FeTS’ federated learning functionality for data discovery through XNAT features through its feature extraction functionality.
Interoperability and privacy preservation algorithmic comparison.
Federated learning for segmentation of PRISM data.
Contact contact [at] fets.ai with any questions.