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