Keywords: Diagnosis/Prediction, Segmentation
Motivation: Site and scanner specific variations in prostate MRI impact performance of deep learning (DL) based models. Federated learning allows for privacy preserving training of DL models without the need for data sharing.
Goal(s): In this study, we train DL models for prostate cancer segmentation on MRI using the Rhino Health federated computing platform.
Approach: We adopt 3D UNet architecture to train the DL models on 2 publicly available datasets.
Results: DL models trained using a federated approach result in more generalizable models compared to those trained on single site data.
Impact: Successful development of deep learning based prostate cancer segmentation models on MRI using federated learning will result in reproducible and generalizable models. These can enhance clinical adoption and potentially improve downstream diagnostic and treatment workflows for prostate cancer.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords