用预训练模型提升联邦学习在肾结石识别中的隐私保护与抗干扰能力
Leveraging Pre-trained Models for Robust Federated Learning for Kidney Stone Type Recognition
- 分两阶段训练：参数优化与鲁棒性验证，结合预训练模型增强泛化能力
- 最高准确率达84.1%（7轮10次迭代），鲁棒验证阶段仍保持77.2%性能
- 适合医疗图像诊断中需保护数据隐私且应对图像质量波动的场景
深度学习显著提升了医学影像诊断的准确性，但受限于大规模数据集需求及数据共享的法律限制。联邦学习（FL）通过分布式训练保障数据隐私，但易受数据污染影响导致性能下降。本文提出一种融合预训练模型的鲁棒联邦学习框架，用于肾结石类型识别。实验使用两个含六类图像的肾结石数据集。方法包含两个阶段：学习参数优化（LPO）与联邦鲁棒性验证（FRV）。LPO阶段在7个周期、10轮通信下达到84.1%最高准确率，FRV阶段稳定在77.2%，表明该框架在图像退化条件下仍具高鲁棒性。结果证明，结合预训练模型与联邦学习可有效解决医疗诊断中的隐私与性能双重挑战，提升系统可信度与患者诊疗质量。
原文摘要 · Abstract (English)
Deep learning developments have improved medical imaging diagnoses dramatically, increasing accuracy in several domains. Nonetheless, obstacles continue to exist because of the requirement for huge datasets and legal limitations on data exchange. A solution is provided by Federated Learning (FL), which permits decentralized model training while maintaining data privacy. However, FL models are susceptible to data corruption, which may result in performance degradation. Using pre-trained models, this research suggests a strong FL framework to improve kidney stone diagnosis. Two different kidney stone datasets, each with six different categories of images, are used in our experimental setting. Our method involves two stages: Learning Parameter Optimization (LPO) and Federated Robustness Validation (FRV). We achieved a peak accuracy of 84.1% with seven epochs and 10 rounds during LPO stage, and 77.2% during FRV stage, showing enhanced diagnostic accuracy and robustness against image corruption. This highlights the potential of merging pre-trained models with FL to address privacy and performance concerns in medical diagnostics, and guarantees improved patient care and enhanced trust in FL-based medical systems.
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