Federated image-to-image MRI translation from heterogeneous multiple-sites data

Motivation: Applying machine learning (ML) in MRI necessitates the development of large and diverse datasets, which is a challenging process. Federated learning (FL) is a new frontier in ML that offers the possibility of multi-site data aggregation. Goal(s): In our study, we examine a traditional deep convolutional neural network applied to multiple sources with that of the FL technique using different aggregation methods. Approach: As a proof-of-concept, we employ four publicly available MRI datasets and carry out image-to-image translation between T1- and T2-weighted scans. Results: Our findings suggest that the FL generalizes the model more effectively than using models trained at each site separately. Impact: Our research demonstrated the crucial role of federated learning in medical imaging. It also emphasized the significance of selecting an appropriate aggregation algorithm considering the data type and degree of heterogeneity.