Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease

Abstract

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 setups , 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.

Decentralized Distributed Multi-institutional PET Image Segmentation Using a Federated Deep Learning Framework

“…One main limitation of the current study was performing all computations on a server with different GPUs simulating different nodes (local computer GPUs were considered as different centers/hospitals) as performed in previous FL studies. [47][48][49][50][51][52][53] A number of challenges were linked to the training of the data sets for implementation of the FL approach, such as local computer capacity and communication between centers and local sites. Further studies should be carried out involving real multiple clinical centers (using one-center leave-out strategy) to tackle these challenges, specifically the communication bottleneck.…”

“…In the present study, all preprocessing was performed in a uniform manner, including converting to SUV, cropping, and normalizing to provide reproducibility across different centers. One main limitation of the current study was performing all computations on a server with different GPUs simulating different nodes (local computer GPUs were considered as different centers/hospitals) as performed in previous FL studies 47–53 . A number of challenges were linked to the training of the data sets for implementation of the FL approach, such as local computer capacity and communication between centers and local sites.…”

“…The model learns from the data sets using SGD for all optimizations. 22 Similar to previous studies, [47][48][49][50][51][52][53] the FL process in this work was performed on a server with multiple local graphics processing units (GPUs), where each local GPU was considered as a different center.…”