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The recent outbreak of COVID-19 has led to urgent needs for reliablediagnosis and management of SARS-CoV-2 infection. As a complimentary tool,chest CT has been shown to be able to reveal visual patterns characteristic forCOVID-19, which has definite value at several stages during the disease course.To facilitate CT analysis, recent efforts have focused on computer-aidedcharacterization and diagnosis, which has shown promising results. However,domain shift of data across clinical data centers poses a serious challengewhen deploying learning-based models. In this work, we attempt to find asolution for this challenge via federated and semi-supervised learning. Amulti-national database consisting of 1704 scans from three countries isadopted to study the performance gap, when training a model with one datasetand applying it to another. Expert radiologists manually delineated 945 scansfor COVID-19 findings. In handling the variability in both the data andannotations, a novel federated semi-supervised learning technique is proposedto fully utilize all available data (with or without annotations). Federatedlearning avoids the need for sensitive data-sharing, which makes it favorablefor institutions and nations with strict regulatory policy on data privacy.Moreover, semi-supervision potentially reduces the annotation burden under adistributed setting. The proposed framework is shown to be effective comparedto fully supervised scenarios with conventional data sharing instead of modelweight sharing.

Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan