  Accurately measuring the evolution of Multiple Sclerosis (MS) with magnetic
resonance imaging (MRI) critically informs understanding of disease progression
and helps to direct therapeutic strategy. Deep learning models have shown
promise for automatically segmenting MS lesions, but the scarcity of accurately
annotated data hinders progress in this area. Obtaining sufficient data from a
single clinical site is challenging and does not address the heterogeneous need
for model robustness. Conversely, the collection of data from multiple sites
introduces data privacy concerns and potential label noise due to varying
annotation standards. To address this dilemma, we explore the use of the
federated learning framework while considering label noise. Our approach
enables collaboration among multiple clinical sites without compromising data
privacy under a federated learning paradigm that incorporates a noise-robust
training strategy based on label correction. Specifically, we introduce a
Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced
distribution and fuzzy boundaries of MS lesions, enabling the correction of
false annotations based on prediction confidence. We also introduce a Centrally
Enhanced Label Correction (CELC) strategy, which leverages the aggregated
central model as a correction teacher for all sites, enhancing the reliability
of the correction process. Extensive experiments conducted on two multi-site
datasets demonstrate the effectiveness and robustness of our proposed methods,
indicating their potential for clinical applications in multi-site
collaborations.
