用数字孪生与联邦学习实现跨机构脑肿瘤精准分割，保护隐私又不降效果。
TwinSegNet: A Digital Twin-Enabled Federated Learning Framework for Brain Tumor Analysis
- 结合卷积编码器与视觉变压器，捕捉局部与全局特征。
- 在9个异构MRI数据集上，Dice达0.90%，敏感度与特异度超90%。
- 适合需要隐私保护的多中心医疗场景，支持个性化建模。
脑肿瘤分割对疾病诊断和治疗规划至关重要。然而，当前深度学习方法依赖集中式数据收集，引发隐私担忧并限制跨机构泛化能力。本文提出TwinSegNet，一种融合混合ViT-UNet模型与个性化数字孪生的隐私保护联邦学习框架，实现精准、实时的脑肿瘤分割。该架构结合卷积编码器与视觉变压器瓶颈，以捕获局部与全局上下文。各机构基于私有数据微调全局模型，形成其数字孪生。在包括BraTS 2019–2021及自建肿瘤数据集在内的九个异构MRI数据集上评估，TwinSegNet取得高达0.90的Dice分数，敏感度与特异性均超过90%，展现出对非独立同分布（non-IID）客户端分布的鲁棒性。与集中式模型TumorVisNet相比，TwinSegNet在不牺牲性能的前提下有效保护隐私。本方法为多机构临床环境提供可扩展、个性化的分割方案，严格遵守数据保密要求。
原文摘要 · Abstract (English)
Brain tumor segmentation is critical in diagnosis and treatment planning for the disease. Yet, current deep learning methods rely on centralized data collection, which raises privacy concerns and limits generalization across diverse institutions. In this paper, we propose TwinSegNet, which is a privacy-preserving federated learning framework that integrates a hybrid ViT-UNet model with personalized digital twins for accurate and real-time brain tumor segmentation. Our architecture combines convolutional encoders with Vision Transformer bottlenecks to capture local and global context. Each institution fine-tunes the global model of private data to form its digital twin. Evaluated on nine heterogeneous MRI datasets, including BraTS 2019-2021 and custom tumor collections, TwinSegNet achieves high Dice scores (up to 0.90%) and sensitivity/specificity exceeding 90%, demonstrating robustness across non-independent and identically distributed (IID) client distributions. Comparative results against centralized models such as TumorVisNet highlight TwinSegNet's effectiveness in preserving privacy without sacrificing performance. Our approach enables scalable, personalized segmentation for multi-institutional clinical settings while adhering to strict data confidentiality requirements.
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