  Medical image segmentation plays a vital role in clinic disease diagnosis and
medical image analysis. However, labeling medical images for segmentation task
is tough due to the indispensable domain expertise of radiologists.
Furthermore, considering the privacy and sensitivity of medical images, it is
impractical to build a centralized segmentation dataset from different medical
institutions. Federated learning aims to train a shared model of isolated
clients without local data exchange which aligns well with the scarcity and
privacy characteristics of medical data. To solve the problem of labeling hard,
many advanced semi-supervised methods have been proposed in a centralized data
setting. As for federated learning, how to conduct semi-supervised learning
under this distributed scenario is worth investigating. In this work, we
propose a novel federated semi-supervised learning framework for medical image
segmentation. The intra-client and inter-client consistency learning are
introduced to smooth predictions at the data level and avoid confirmation bias
of local models. They are achieved with the assistance of a Variational
Autoencoder (VAE) trained collaboratively by clients. The added VAE model plays
three roles: 1) extracting latent low-dimensional features of all labeled and
unlabeled data; 2) performing a novel type of data augmentation in calculating
intra-client consistency loss; 3) utilizing the generative ability of itself to
conduct inter-client consistency distillation. The proposed framework is
compared with other federated semi-supervised or self-supervised learning
methods. The experimental results illustrate that our method outperforms the
state-of-the-art method while avoiding a lot of computation and communication
overhead.
