A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm.

ABSTRACT: Distributed learning is a promising alternative to central learning for machine learning (ML) model training, overcoming data-sharing problems in healthcare. Previous studies exploring federated learning (FL) or the traveling model (TM) setup for medical image-based disease classification often relied on large databases with a limited number of centers or simulated artificial centers, raising doubts about real-world applicability. This study develops and evaluates a convolution neural network (CNN) for Parkinson's disease classification using data acquired by 83 diverse real centers around the world, mostly contributing small training samples. Our approach specifically makes use of the TM setup, which has proven effective in scenarios with limited data availability but has never been used

SUBMITTER: Souza R 

PROVIDER: S-EPMC10879577 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature