  Deep learning models can enable accurate and efficient disease diagnosis, but
have thus far been hampered by the data scarcity present in the medical world.
Automated diagnosis studies have been constrained by underpowered single-center
datasets, and although some results have shown promise, their generalizability
to other institutions remains questionable as the data heterogeneity between
institutions is not taken into account. By allowing models to be trained in a
distributed manner that preserves patients' privacy, federated learning
promises to alleviate these issues, by enabling diligent multi-center studies.
We present the first federated learning study on the modality of cardiovascular
magnetic resonance (CMR) and use four centers derived from subsets of the M\&M
and ACDC datasets, focusing on the diagnosis of hypertrophic cardiomyopathy
(HCM). We adapt a 3D-CNN network pretrained on action recognition and explore
two different ways of incorporating shape prior information to the model, and
four different data augmentation set-ups, systematically analyzing their impact
on the different collaborative learning choices. We show that despite the small
size of data (180 subjects derived from four centers), the privacy preserving
federated learning achieves promising results that are competitive with
traditional centralized learning. We further find that federatively trained
models exhibit increased robustness and are more sensitive to domain shift
effects.
