标题： FIVA：具有不确定性估计的通用CT分割联邦逆方差平均方法 显示英文标题

标题： FIVA: Federated Inverse Variance Averaging for Universal CT Segmentation with Uncertainty Estimation

摘要： 不同CT分割数据集通常来自不同的扫描仪，在不同的采集设置下获取，并且通常只提供有限的且常常不重叠的器官分割标签。 在有效利用这些异构数据的同时保护患者隐私可能具有挑战性。 本工作提出了一种新颖的联邦学习方法，通过利用模型不确定性进行聚合和预测不确定性进行推理，实现在多样化腹部CT数据集上的通用分割。 我们的方法利用随机小批量梯度下降中的固有噪声来估计模型权重的分布，以在客户端级别提供对模型参数的实时不确定性。 然后，使用额外的不确定性信息，在服务器端使用一种受贝叶斯启发的逆方差聚合方案对这些参数进行聚合。 此外，所提出的方法通过从模型权重传播不确定性来量化预测不确定性，为临床决策提供了必要的置信度度量。 与最近的研究一致，预测不确定性在推理阶段被用于提高预测性能。 实验评估表明，与之前建立的基线相比，该方法在提高联邦聚合质量和不确定性加权推理方面是有效的。 本工作的代码已发布在：https://github.com/asimukaye/fiva 显示英文摘要

摘要： Different CT segmentation datasets are typically obtained from different scanners under different capture settings and often provide segmentation labels for a limited and often disjoint set of organs. Using these heterogeneous data effectively while preserving patient privacy can be challenging. This work presents a novel federated learning approach to achieve universal segmentation across diverse abdominal CT datasets by utilizing model uncertainty for aggregation and predictive uncertainty for inference. Our approach leverages the inherent noise in stochastic mini-batch gradient descent to estimate a distribution over the model weights to provide an on-the-go uncertainty over the model parameters at the client level. The parameters are then aggregated at the server using the additional uncertainty information using a Bayesian-inspired inverse-variance aggregation scheme. Furthermore, the proposed method quantifies prediction uncertainty by propagating the uncertainty from the model weights, providing confidence measures essential for clinical decision-making. In line with recent work shown, predictive uncertainty is utilized in the inference stage to improve predictive performance. Experimental evaluations demonstrate the effectiveness of this approach in improving both the quality of federated aggregation and uncertainty-weighted inference compared to previously established baselines. The code for this work is made available at: https://github.com/asimukaye/fiva