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Healthcare data is often split into medium/small-sized collections acrossmultiple hospitals and access to it is encumbered by privacy regulations. Thisbrings difficulties to use them for the development of machine learning anddeep learning models, which are known to be data-hungry. One way to overcomethis limitation is to use collaborative learning (CL) methods, which allowhospitals to work collaboratively to solve a task, without the need toexplicitly share local data.  In this paper, we address a prostate segmentation problem from MRI in acollaborative scenario by comparing two different approaches: federatedlearning (FL) and consensus-based methods (CBM).  To the best of our knowledge, this is the first work in which CBM, such aslabel fusion techniques, are used to solve a problem of collaborative learning.In this setting, CBM combine predictions from locally trained models to obtaina federated strong learner with ideally improved robustness and predictivevariance properties.  Our experiments show that, in the considered practical scenario, CBMs provideequal or better results than FL, while being highly cost-effective. Our resultsdemonstrate that the consensus paradigm may represent a valid alternative to FLfor typical training tasks in medical imaging.

Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation