Federated learning (FL) for histopathology image segmentation involving multiple medical sites plays a crucial role in advancing the field of accurate disease diagnosis and treatment. However, it is still a task of great challenges due to the sample imbalance across clients and large data heterogeneity from disparate organs, variable segmentation tasks, and diverse distribution. Thus, we propose a novel FL approach for histopathology nuclei and tissue segmentation, FedSODA, via synthetic-driven cross-assessment operation (SO) and dynamic stratified-layer aggregation (DA). Our SO constructs a cross-assessment strategy to connect clients and mitigate the representation bias under sample imbalance. Our DA utilizes layer-wise interaction and dynamic aggregation to diminish heterogeneity and enhance generalization. The effectiveness of our FedSODA has been evaluated on the most extensive histopathology image segmentation dataset from 7 independent datasets. The code is available at https://github.com/yuanzhang7/FedSODA.

翻译：联邦学习（FL）在涉及多医疗机构的组织病理学图像分割中，对推动疾病精确诊断与治疗领域的发展至关重要。然而，由于各客户端样本不平衡、来自不同器官的庞大数据异质性、多变的组织分割任务及多样化分布，该任务仍面临巨大挑战。为此，我们提出一种新颖的联邦学习方法FedSODA，用于组织病理学细胞核与组织分割，该方法通过合成驱动的交叉评估操作（SO）与动态分层聚合（DA）实现。其中，SO构建了一种跨客户端交叉评估策略，以缓解样本不平衡下的表征偏差；DA通过逐层交互与动态聚合来降低异质性并增强泛化能力。我们在包含7个独立数据集的规模最大的组织病理学图像分割数据集上验证了FedSODA的有效性。代码已开源至https://github.com/yuanzhang7/FedSODA。