  Chest Computational Tomography (CT) scans present low cost, speed and
objectivity for COVID-19 diagnosis and deep learning methods have shown great
promise in assisting the analysis and interpretation of these images. Most
hospitals or countries can train their own models using in-house data, however
empirical evidence shows that those models perform poorly when tested on new
unseen cases, surfacing the need for coordinated global collaboration. Due to
privacy regulations, medical data sharing between hospitals and nations is
extremely difficult. We propose a GAN-augmented federated learning model,
dubbed ST-FL (Style Transfer Federated Learning), for COVID-19 image
segmentation. Federated learning (FL) permits a centralised model to be learned
in a secure manner from heterogeneous datasets located in disparate private
data silos. We demonstrate that the widely varying data quality on FL client
nodes leads to a sub-optimal centralised FL model for COVID-19 chest CT image
segmentation. ST-FL is a novel FL framework that is robust in the face of
highly variable data quality at client nodes. The robustness is achieved by a
denoising CycleGAN model at each client of the federation that maps arbitrary
quality images into the same target quality, counteracting the severe data
variability evident in real-world FL use-cases. Each client is provided with
the target style, which is the same for all clients, and trains their own
denoiser. Our qualitative and quantitative results suggest that this FL model
performs comparably to, and in some cases better than, a model that has
centralised access to all the training data.
