FedLPPA：为联邦弱监督医学图像分割学习个性化提示与聚合
FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation
AI总结：
提出个性化联邦学习框架FedLPPA，通过可学习提示与双注意力机制适应异构弱监督，结合双解码器策略优化伪标签生成，在多模态医学图像分割任务中表现接近全监督集中训练。
摘要：
联邦学习（FL）有效缓解了政策和隐私担忧带来的数据孤岛挑战，隐式地为深度模型训练利用了更多数据。然而，传统的集中式FL模型难以应对多样化的多中心数据，特别是在面临显著的数据异质性时，尤其是在医学场景中。在医学图像分割领域，降低标注成本的迫切需求日益增长，这放大了利用点、涂鸦等稀疏标注的弱监督技术的重要性。实用的FL范式应能适应不同站点的多样化标注格式，这一研究课题仍有待深入探索。在此背景下，我们提出了一种新颖的个性化FL框架，带有可学习提示和聚合（FedLPPA），以统一利用异构弱监督进行医学图像分割。在FedLPPA中，维护了一个可学习的通用知识提示，并辅以多个可学习的个性化数据分布提示和表示监督稀疏性的提示。通过双注意力机制与样本特征整合，这些提示使每个本地任务解码器能够熟练地适应本地分布和监督形式。同时，基于提示相似性引入了双解码器策略，以增强弱监督学习中伪标签的生成，缓解本地数据固有的过拟合和噪声积累，并采用自适应聚合方法在参数层面上定制任务解码器。在涉及不同模态的四个不同医学图像分割任务上的大量实验突显了FedLPPA的优越性，其有效性紧密平行于完全监督的集中式训练。我们的代码和数据将公开提供。
英文摘要：
Federated learning (FL) effectively mitigates the data silo challenge brought about by policies and privacy concerns, implicitly harnessing more data for deep model training. However, traditional centralized FL models grapple with diverse multi-center data, especially in the face of significant data heterogeneity, notably in medical contexts. In the realm of medical image segmentation, the growing imperative to curtail annotation costs has amplified the importance of weakly-supervised techniques which utilize sparse annotations such as points, scribbles, etc. A pragmatic FL paradigm shall accommodate diverse annotation formats across different sites, which research topic remains under-investigated. In such context, we propose a novel personalized FL framework with learnable prompt and aggregation (FedLPPA) to uniformly leverage heterogeneous weak supervision for medical image segmentation. In FedLPPA, a learnable universal knowledge prompt is maintained, complemented by multiple learnable personalized data distribution prompts and prompts representing the supervision sparsity. Integrated with sample features through a dual-attention mechanism, those prompts empower each local task decoder to adeptly adjust to both the local distribution and the supervision form. Concurrently, a dual-decoder strategy, predicated on prompt similarity, is introduced for enhancing the generation of pseudo-labels in weakly-supervised learning, alleviating overfitting and noise accumulation inherent to local data, while an adaptable aggregation method is employed to customize the task decoder on a parameter-wise basis. Extensive experiments on four distinct medical image segmentation tasks involving different modalities underscore the superiority of FedLPPA, with its efficacy closely parallels that of fully supervised centralized training. Our code and data will be available.
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