Federated Dual-Attention Segmentation for Privacy-Preserving Multi-Center Pulmonary Nodule Analysis in Computed Tomography Imaging
Keywords:
federated learning, dual-attention, pulmonary nodule segmentation, privacy-preserving, multi-center analysis, computed tomography, healthcare infrastructureAbstract
The increasing adoption of computed tomography (CT) imaging for lung cancer screening has generated vast repositories of pulmonary nodule data across multiple clinical institutions. Centralized aggregation of such sensitive medical data poses significant privacy, legal, and operational challenges, while the heterogeneity of imaging protocols and patient populations across centers complicates the development of robust segmentation models. This paper presents a system-level framework that integrates federated learning with a dual-attention segmentation architecture to enable privacy-preserving, multi-center analysis of pulmonary nodules. The proposed approach decouples model training from direct data sharing, allowing institutions to collaboratively refine a shared model while retaining raw images on-site. The dual-attention mechanism, combining spatial and channel attention, enhances the model’s ability to capture subtle nodule features and reduces false positives, a critical requirement for clinical deployment. This paper examines the structural trade-offs inherent in federated systems, including communication efficiency, model convergence, and differential privacy guarantees. It further discusses the infrastructure and governance necessary for sustaining such a framework across heterogeneous healthcare networks, addressing robustness to domain shifts, fairness across demographic groups, and policy implications for regulatory compliance. By analyzing real-world deployment scenarios and cross-domain comparisons with centralized and other distributed approaches, the paper highlights how federated dual-attention segmentation can balance diagnostic accuracy with patient privacy. The study also identifies open challenges in scalability, adversarial robustness, and equitable performance, and proposes forward-looking strategies for integrating emerging privacy technologies and standardized data formats. This work aims to provide a comprehensive reference for researchers and practitioners designing next-generation collaborative medical imaging systems.
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