Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation

• URL: http://arxiv.org/abs/2502.07951v1
• Date: Tue, 11 Feb 2025 21:00:01 GMT
• Title: Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation
• Authors: Xinyi Tan, Jiacheng Wang, Liansheng Wang,
• Abstract summary: Federated learning (FL) is a formidable solution to this privacy conundrum. We propose a Federated self-supervised Domain Generalization method (LFDG) to enhance the generalization capacity of FL. Our method achieves 3.80% and 3.92% better than the baseline and other recent FL methods and SSL methods, respectively.
• Score: 12.12743798858467
• License:
• Abstract: 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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