Abstract
The application of Digital Twin (DT) technology and Federated Learning (FL) has great potential to change the field of biomedical image analysis, particularly for Computed Tomography (CT) scans. This paper presents Federated Transfer Learning (FTL) as a new Digital Twin-based CT scan analysis paradigm. FTL uses pre-trained models and knowledge transfer between peer nodes to solve problems such as data privacy, limited computing resources, and data heterogeneity. The proposed framework allows real-time collaboration between cloud servers and Digital Twin-enabled CT scanners while protecting patient identity. We apply the FTL method to a heterogeneous CT scan dataset and assess model performance using convergence time, model accuracy, precision, recall, F1 score, and confusion matrix. It has been shown to perform better than conventional FL and Clustered Federated Learning (CFL) methods with better precision, accuracy, recall, and F1-score. The technique is beneficial in settings where the data is not independently and identically distributed (non-IID), and it offers reliable, efficient, and secure solutions for medical diagnosis. These findings highlight the possibility of using FTL to improve decision-making in digital twin-based CT scan analysis, secure and efficient medical image analysis, promote privacy, and open new possibilities for applying precision medicine and smart healthcare systems.
Abstract (translated)
数字孪生（DT）技术和联邦学习（FL）的应用在生物医学图像分析领域，特别是计算机断层扫描（CT）方面具有巨大的变革潜力。本文提出了一种基于数字孪生的CT扫描分析新范式——联合迁移学习（FTL）。该方法利用预训练模型和同级节点之间的知识转移来解决数据隐私、计算资源有限以及数据异质性等问题。所提出的框架允许云端服务器与具备数字孪生功能的CT扫描仪进行实时协作，同时保护患者身份信息。 我们将FTL方法应用于一个异构CT扫描数据集，并通过收敛时间、模型精度、精确度、召回率、F1分数和混淆矩阵来评估模型性能。结果显示，该方法在精度、准确性和召回率方面优于传统的FL和集群联邦学习（CFL）方法，并且具有更高的F1分数。 当数据分布不是独立同质时，此技术尤为有益。它提供了可靠、高效且安全的解决方案用于医学诊断。这些发现强调了使用FTL来改善基于数字孪生的CT扫描分析决策制定的可能性，以及在确保隐私和提高效率的同时进行安全高效的医疗图像分析，并为精准医疗和智慧医疗系统的应用开辟新的可能。
URL
https://arxiv.org/abs/2509.08018
