Self-Supervised Federated Adaptation for Multi-Site Brain Disease Diagnosis

The multi-site approach has attracted increasing attention in brain disease diagnosis, because it can improve the prediction performance by integrating sample information from different medical institutions. However, its training procedure requires the transmission of subject's original images or features among sites, which may cause privacy disclosure. In this article, we propose a self-supervised federated adaptation (S2FA) framework for robust multi-site prediction, which can reduce the risk of privacy disclosure. As far as we know, it is the first work to investigate the cross-site brain disease diagnosis, which trains model on source sites and tests on target site, often occurring in clinical practice. First, we implement a decentralized federated optimization strategy, by which each site communicates model parameters periodically. Second, we construct an auxiliary self-supervised model for target site through transferring knowledge from source sites with self-paced learning. Then, a hash mapping is proposed to encode the target feature, simultaneously reducing the risk of privacy information disclosure and alleviating data heterogeneity among sites. Finally, we achieve the cross-site prediction by weighted federated source model and auxiliary target model. Experimental results on multi-site datasets show that the proposed S2FA can accurately identify brain disease. Our codes are available at https://github.com/nuaayqm/S2FA.