解决医疗影像联邦学习中部分标注数据难题，提升分割精度与泛化能力。
Federated Learning with Partially Labeled Data: A Conditional Distillation Approach
- 引入条件蒸馏机制，缓解部分标注下的模型退化问题。
- 在3D CT和2D胸片数据上显著提升分割准确率，优于现有方法。
- 适合隐私敏感的多中心医疗协作，支持未见扫描相位的适应能力。
在医学影像领域，构建能泛化处理多种器官与病灶的分割模型至关重要。然而，全标注数据稀缺及严格的隐私法规限制了数据共享。联邦学习（FL）支持去中心化训练，但现有方法在部分标注场景下易出现模型分歧与灾难性遗忘。本文提出ConDistFL，一种融合条件蒸馏的新型联邦学习框架，可有效利用部分标注数据，在分布式非均匀数据上显著提升分割性能。除优异的分割表现外，ConDistFL保持计算与通信效率，具备良好可扩展性。实验表明，其在跨联盟测试中展现出卓越泛化能力，甚至能适应未见对比相位（如非增强CT图像）。在3D CT与2D胸片数据集上的大量评估验证了ConDistFL在隐私约束环境下实现高效、灵活的协作医学图像分割的可行性。
原文摘要 · Abstract (English)
In medical imaging, developing generalized segmentation models that can handle multiple organs and lesions is crucial. However, the scarcity of fully annotated datasets and strict privacy regulations present significant barriers to data sharing. Federated Learning (FL) allows decentralized model training, but existing FL methods often struggle with partial labeling, leading to model divergence and catastrophic forgetting. We propose ConDistFL, a novel FL framework incorporating conditional distillation to address these challenges. ConDistFL enables effective learning from partially labeled datasets, significantly improving segmentation accuracy across distributed and non-uniform datasets. In addition to its superior segmentation performance, ConDistFL maintains computational and communication efficiency, ensuring its scalability for real-world applications. Furthermore, ConDistFL demonstrates remarkable generalizability, significantly outperforming existing FL methods in out-of-federation tests, even adapting to unseen contrast phases (e.g., non-contrast CT images) in our experiments. Extensive evaluations on 3D CT and 2D chest X-ray datasets show that ConDistFL is an efficient, adaptable solution for collaborative medical image segmentation in privacy-constrained settings.
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