Abbas H Zaidi, Susan F Saleeb, Michelle Gurvitz, Emily Bucholz, Kimberlee Gauvreau, Kathy J Jenkins, Sarah D de Ferranti
Social Determinants of Health Including Child Opportunity Index Leading to Gaps in Care for Patients With Significant Congenital Heart Disease
Journal of the American Heart Association
accurately measuring evolution multiple sclerosis ms magnetic resonance imaging mri critically informs understanding disease progression helps direct therapeutic strategy deep learning models shown promise automatically segmenting ms lesions scarcity accurately annotated data hinders progress area obtaining sufficient data single clinical site challenging address heterogeneous need model robustness conversely collection data multiple sites introduces data privacy concerns potential label noise due varying annotation standards address dilemma explore use federated learning framework considering label noise approach enables collaboration among multiple clinical sites without compromising data privacy federated learning paradigm incorporates noiserobust training strategy based label correction specifically introduce decoupled hard label correction dhlc strategy considers imbalanced distribution fuzzy boundaries ms lesions enabling correction false annotations based prediction confidence also introduce centrally enhanced label correction celc strategy leverages aggregated central model correction teacher sites enhancing reliability correction process extensive experiments conducted two multisite datasets demonstrate effectiveness robustness proposed methods indicating potential clinical applications multisite collaborations train better deep learning models lower cost data collection annotation