FedMed-GAN: Federated domain translation on unsupervised cross- modality brain image synthesis

Utilizing multi-modal neuroimaging data is proven to be effective in investigating human cognitive activ-ities and certain pathologies. However, it is not practical to obtain the full set of paired neuroimaging data centrally since the collection faces several constraints, e.g., high examination cost, long acquisition time, and image corruption. In addition, these data are dispersed into different medical institutions and thus cannot be aggregated for centralized training considering the privacy issues. There is a clear need to launch federated learning and facilitate the integration of dispersed data from different institutions. In this paper, we propose a new benchmark for federated domain translation on unsupervised brain image synthesis (FedMed-GAN) to bridge the gap between federated learning and medical GAN. FedMed-GAN mitigates the mode collapse without sacrificing the performance of generators, and is widely applied to different proportions of unpaired and paired data with variation adaptation properties. We treat the gradient penalties using the federated averaging algorithm and then leverage the differential privacy gra-dient descent to regularize the training dynamics. A comprehensive evaluation is provided for comparing FedMed-GAN and other centralized methods, demonstrating that the proposed algorithm outperforms the state-of-the-art. Our code is available at: https://github.com/M-3LAB/FedMed-GAN.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

Utilizing multi-modal neuroimaging data is effective in studying human cognitive activities and pathologies, but obtaining full sets of centrally collected paired data is impractical. Federated learning is needed to integrate dispersed data from different institutions. The proposed FedMed-GAN algorithm bridges the gap between federated learning and medical GAN, mitigating mode collapse without sacrificing generator performance. It outperforms state-of-the-art methods in comprehensive evaluations.