提出联邦自监督域泛化方法，提升医疗肠息肉分割的泛化能力。
Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation
- 通过对抗式数据增强提升联邦学习中的数据多样性
- 在六家医院数据上比基线高3.80%，优于现有FL与SSL方法
- 适合隐私敏感场景下的标签高效医学图像分割任务
在构建基于深度学习的自动肠息肉分割模型时，自监督学习（SSL）对处理无标签息肉数据至关重要。然而，医疗数据涉及复杂隐私问题，不同医疗机构间难以实现数据共享。联邦学习（FL）为此提供了解决方案，但如何在联邦设置下提升模型泛化能力仍面临挑战。本文提出一种联邦自监督域泛化方法LFDG，用于提升标签高效且具备泛化能力的肠息肉分割性能。基于经典SSL方法DropPos，LFDG引入基于对抗学习的数据增强方法（SSADA），以增强数据多样性；同时设计基于源重建与增强掩码的松弛模块（SRAM），稳定特征学习过程。在来自六家医疗中心的息肉图像上验证，该方法性能分别优于基线和其他近期FL与SSL方法3.80%和3.92%。
原文摘要 · Abstract (English)
Employing self-supervised learning (SSL) methodologies assumes par-amount significance in handling unlabeled polyp datasets when building deep learning-based automatic polyp segmentation models. However, the intricate privacy dynamics surrounding medical data often preclude seamless data sharing among disparate medical centers. Federated learning (FL) emerges as a formidable solution to this privacy conundrum, yet within the realm of FL, optimizing model generalization stands as a pressing imperative. Robust generalization capabilities are imperative to ensure the model's efficacy across diverse geographical domains post-training on localized client datasets. In this paper, a Federated self-supervised Domain Generalization method is proposed to enhance the generalization capacity of federated and Label-efficient intestinal polyp segmentation, named LFDG. Based on a classical SSL method, DropPos, LFDG proposes an adversarial learning-based data augmentation method (SSADA) to enhance the data diversity. LFDG further proposes a relaxation module based on Source-reconstruction and Augmentation-masking (SRAM) to maintain stability in feature learning. We have validated LFDG on polyp images from six medical centers. The performance of our method achieves 3.80% and 3.92% better than the baseline and other recent FL methods and SSL methods, respectively.
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