论文标题
对联合脑肿瘤分割的不同聚合和超参数选择方法的评估和分析
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation
论文作者
论文摘要
大型，多样化和跨国数据集的可用性对于在医学成像域中开发有效和临床适用的AI系统至关重要。但是，通过将这些数据集汇集到中心位置来形成全球模型，并带有各种数据隐私和所有权问题。为了减轻这些问题，最近的一些研究集中于联邦学习范式，这是一种分散数据的分布式学习方法。联合学习利用所有可用的数据，无需与彼此共享合作者的数据或在中央服务器上收集它们。研究表明，联邦学习可以通过常规中央培训提供竞争性能，同时具有良好的概括能力。在这项工作中，我们研究了几种有关脑肿瘤分割问题的联邦学习方法。我们探索了更快的融合和更好绩效的不同策略，这也可以在强大的非IID案例中起作用。
Availability of large, diverse, and multi-national datasets is crucial for the development of effective and clinically applicable AI systems in the medical imaging domain. However, forming a global model by bringing these datasets together at a central location, comes along with various data privacy and ownership problems. To alleviate these problems, several recent studies focus on the federated learning paradigm, a distributed learning approach for decentralized data. Federated learning leverages all the available data without any need for sharing collaborators' data with each other or collecting them on a central server. Studies show that federated learning can provide competitive performance with conventional central training, while having a good generalization capability. In this work, we have investigated several federated learning approaches on the brain tumor segmentation problem. We explore different strategies for faster convergence and better performance which can also work on strong Non-IID cases.
