  Federated learning and its application to medical image segmentation have
recently become a popular research topic. This training paradigm suffers from
statistical heterogeneity between participating institutions' local datasets,
incurring convergence slowdown as well as potential accuracy loss compared to
classical training. To mitigate this effect, federated personalization emerged
as the federated optimization of one model per institution. We propose a novel
personalization algorithm tailored to the feature shift induced by the usage of
different scanners and acquisition parameters by different institutions. This
method is the first to account for both inter and intra-institution feature
shift (multiple scanners used in a single institution). It is based on the
computation, within each centre, of a series of radiomic features capturing the
global texture of each 3D image volume, followed by a clustering analysis
pooling all feature vectors transferred from the local institutions to the
central server. Each computed clustered decentralized dataset (potentially
including data from different institutions) then serves to finetune a global
model obtained through classical federated learning. We validate our approach
on the Federated Brain Tumor Segmentation 2022 Challenge dataset (FeTS2022).
Our code is available at (https://github.com/MatthisManthe/radiomics_CFFL).
