去中心化医疗影像诊断框架，保护隐私且高效训练
Simplified Swarm Learning Framework for Robust and Scalable Diagnostic Services in Cancer Histopathology
- 摒弃区块链改用轻量级点对点通信，提升可扩展性
- 在癌症病理图像上达到与集中式模型相当的准确率
- 适合资源受限医院，兼顾隐私与诊断效率
医疗数据的复杂性，包括隐私问题、数据不平衡和互操作性挑战，亟需创新的机器学习解决方案。蜂群学习（Swarm Learning, SL）作为联邦学习的去中心化替代方案，虽能实现隐私保护的分布式训练，但依赖区块链技术限制了其可及性和可扩展性。本文提出一种面向资源受限环境的简化点对点蜂群学习（P2P-SL）框架，通过去除区块链依赖并采用轻量级点对点通信，确保模型同步的鲁棒性同时保障数据隐私。该框架应用于癌症病理学，结合优化的预训练模型（如TorchXRayVision），并引入DenseNet解码器以提升诊断准确性。大量实验表明，该框架在处理不平衡和偏倚数据集时表现优异，性能接近集中式模型，同时保持隐私安全。本研究为医疗领域普及先进机器学习提供了可扩展、易访问且高效的解决方案。
原文摘要 · Abstract (English)
The complexities of healthcare data, including privacy concerns, imbalanced datasets, and interoperability issues, necessitate innovative machine learning solutions. Swarm Learning (SL), a decentralized alternative to Federated Learning, offers privacy-preserving distributed training, but its reliance on blockchain technology hinders accessibility and scalability. This paper introduces a \textit{Simplified Peer-to-Peer Swarm Learning (P2P-SL) Framework} tailored for resource-constrained environments. By eliminating blockchain dependencies and adopting lightweight peer-to-peer communication, the proposed framework ensures robust model synchronization while maintaining data privacy. Applied to cancer histopathology, the framework integrates optimized pre-trained models, such as TorchXRayVision, enhanced with DenseNet decoders, to improve diagnostic accuracy. Extensive experiments demonstrate the framework's efficacy in handling imbalanced and biased datasets, achieving comparable performance to centralized models while preserving privacy. This study paves the way for democratizing advanced machine learning in healthcare, offering a scalable, accessible, and efficient solution for privacy-sensitive diagnostic applications.
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