FedMust：用于多器官CT分割的半监督多任务师生联邦学习
FedMust: Semi-supervised Multi-task Student-Teacher Federated Learning for Multi-organ CT Segmentation
- Norwegian University of Science and Technology（挪威科技大学）
- St. Olavs Hospital, Trondheim University Hospital（圣奥拉夫医院，特隆赫姆大学医院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对多器官CT分割中标注数据不足和隐私限制问题，提出半监督联邦多任务师生框架FedMust，利用标注和未标注数据，通过双联邦机制提升性能，平均提升13%。
AI中文摘要：
使用深度学习进行多器官分割需要大量带标注的患者数据；然而，医疗机构通常缺乏足够大且多样化的标注数据集。隐私约束进一步阻止机构共享患者数据以克服这一限制。此外，由于标注工作的劳动密集性以及多样化专业知识的稀缺，机构通常只对其本地数据的一小部分进行标注，而留下更大的未标注部分未使用。在这项工作中，我们提出了一个灵活的半监督联邦多任务师生框架，该框架利用联邦学习（FL）来利用参与站点中的标注和未标注数据改进多器官分割。在每一轮通信中，所提出的框架启动本地训练，其中具有相同任务标注的客户端形成一个联邦以产生一个聚合的教师模型。生成的教师模型为每个客户端的所有数据生成任务特定特征。随后，所有客户端形成第二个联邦来训练一个多任务学生模型，该模型具有共享编码器和任务特定解码器，以在所有分割任务上复制教师生成的特征。然后使用聚合的学生模型来更新本地教师模型并启动下一轮训练。大量实验证明了所提出的方法相对于本地和联邦单器官模型的有效性，在客户端上平均性能提升13%。实验还证明了多任务学习和未标注数据的影响，以及该框架在放宽客户端参与标注数据要求方面的适用性。代码可在以下网址获取：此https URL。
英文摘要：
Multi-organ segmentation using deep learning requires large amounts of annotated patient data; however, institutions often lack sufficiently large and diverse annotated datasets. Privacy constraints further prevent institutions from sharing patient data to overcome this limitation. Moreover, due to the labor-intensive nature of annotation and the scarcity of diverse expertise, institutions typically have labels for only a small portion of their local data, leaving the larger unlabeled portion unused. In this work, we propose a flexible semi-supervised federated multi-task student-teacher framework that leverages federated learning (FL) to improve multi-organ segmentation using both labeled and unlabeled data across participating sites. At each communication round, the proposed framework initiates local training, where clients with labels for the same task form a federation to produce an aggregated teacher model. The resulting teachers generate task-specific features for all data at each client. Subsequently, all clients form a second federation to train a multi-task student model with a shared encoder and task-specific decoders that replicate the teacher-generated features across all segmentation tasks. The aggregated student model is then used to update the local teachers and initiate the next training round. Extensive experiments demonstrated the effectiveness of the proposed method compared with local and federated single-organ models, yielding an average performance gain of 13 percent across clients. The experiments also demonstrated the impact of multi-task learning and unlabeled data and the applicability of the framework in relaxing labeled-data requirements for client participation. The code is available at https://github.com/AshknMrd/FedMust.
