Partially supervised segmentation is a label-saving method based on datasets with fractional classes labeled and intersectant. However, it is still far from landing on real-world medical applications due to privacy concerns and data heterogeneity. As a remedy without privacy leakage, federated partially supervised segmentation (FPSS) is formulated in this work. The main challenges for FPSS are class heterogeneity and client drift. We propose a Unified Federated Partially-labeled Segmentation (UFPS) framework to segment pixels within all classes for partially-annotated datasets by training a totipotential global model without class collision. Our framework includes Unified Label Learning and sparsed Unified Sharpness Aware Minimization for unification of class and feature space, respectively. We find that vanilla combinations for traditional methods in partially supervised segmentation and federated learning are mainly hampered by class collision through empirical study. Our comprehensive experiments on real medical datasets demonstrate better deconflicting and generalization ability of UFPS compared with modified methods.

翻译：摘要：部分监督分割是一种基于类别标签部分标注且存在重叠的数据集实现的标签高效方法。然而，由于隐私问题与数据异构性，该方法仍难以落地于现实医疗应用。为在无隐私泄露前提下解决此问题，本文首次提出联邦部分监督分割（FPSS）方法。FPSS面临的主要挑战包括类别异构性与客户端漂移。我们提出统一联邦部分标注分割（UFPS）框架，通过训练一个无类别冲突的全能全局模型，对部分标注数据集中的所有类别像素进行分割。该框架分别采用统一标签学习与稀疏化统一锐度感知最小化，实现类别空间与特征空间的统一。实证研究表明，传统部分监督分割与联邦学习方法直接组合的主要障碍在于类别冲突。在真实医疗数据集上的综合实验表明，相较于改进方法，UFPS具有更优的去冲突与泛化能力。