  In recent years, data-driven machine learning (ML) methods have
revolutionized the computer vision community by providing novel efficient
solutions to many unsolved (medical) image analysis problems. However, due to
the increasing privacy concerns and data fragmentation on many different sites,
existing medical data are not fully utilized, thus limiting the potential of
ML. Federated learning (FL) enables multiple parties to collaboratively train a
ML model without exchanging local data. However, data heterogeneity (non-IID)
among the distributed clients is yet a challenge. To this end, we propose a
novel federated method, denoted Federated Disentanglement (FedDis), to
disentangle the parameter space into shape and appearance, and only share the
shape parameter with the clients. FedDis is based on the assumption that the
anatomical structure in brain MRI images is similar across multiple
institutions, and sharing the shape knowledge would be beneficial in anomaly
detection. In this paper, we leverage healthy brain scans of 623 subjects from
multiple sites with real data (OASIS, ADNI) in a privacy-preserving fashion to
learn a model of normal anatomy, that allows to segment abnormal structures. We
demonstrate a superior performance of FedDis on real pathological databases
containing 109 subjects; two publicly available MS Lesions (MSLUB, MSISBI), and
an in-house database with MS and Glioblastoma (MSI and GBI). FedDis achieved an
average dice performance of 0.38, outperforming the state-of-the-art (SOTA)
auto-encoder by 42% and the SOTA federated method by 11%. Further, we
illustrate that FedDis learns a shape embedding that is orthogonal to the
appearance and consistent under different intensity augmentations.
