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

翻译：医疗数据通常分散在多家医院的中小型数据集中，且受隐私法规限制难以访问。这给依赖大量数据的机器学习和深度学习模型开发带来了困难。克服这一局限的方法之一是采用协作学习（CL）方法，该方法允许医院在不需显式共享本地数据的情况下协作解决任务。本文通过比较联邦学习（FL）和基于共识的方法（CBM）两种不同方法，研究了磁共振成像（MRI）中前列腺分割问题的协作场景。据我们所知，这是首次将标签融合技术等CBM应用于解决协作学习问题。在此设置中，CBM通过结合本地训练模型的预测结果，获得一个联邦强学习器，理论上可提升鲁棒性和预测方差特性。实验表明，在所考虑的实际场景中，CBM在高度成本效益的前提下，能提供与FL相当或更优的结果。我们的研究证实，共识范式可能成为医学影像典型训练任务中联邦学习的有效替代方案。