Abstract
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: this https URL
Abstract (translated)
不同的CT分割数据集通常来自不同类型的扫描仪和不同的采集设置，并且往往只为有限的、不连续的一组器官提供分割标签。有效地使用这些异质性数据并同时保护患者隐私是一项挑战。这项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和预测不确定性来实现跨多样腹部CT数据集的通用分割。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上的分布，从而在客户端级别实时提供关于模型参数的不确定性。然后，在服务器端使用基于贝叶斯的思想和逆方差聚合方案来整合这些参数，并结合额外的不确定性信息进行汇总。 此外，所提出的方法通过从模型权重中传播不确定性来量化预测中的不确定性和置信度量，这对临床决策至关重要。正如最近的工作所示，我们在推理阶段利用了预测的不确定性以提高预测性能。实验评估表明，与先前建立的基础方法相比，该方法在改善联邦聚合质量和基于不确定性的加权推断方面是有效的。 此工作的代码可在以下网址获取：this https URL
URL
https://arxiv.org/abs/2508.09196
