用联邦量子网络实现隐私保护的糖尿病视网膜病变早期检测
FQPDR: Federated Quantum Neural Network for Privacy-preserving Early Detection of Diabetic Retinopathy
- 基于联邦学习与量子神经网络构建轻量模型
- 在Kaggle数据集上跨设备评估表现稳定
- 适合医疗数据隐私敏感场景的早期筛查
糖尿病视网膜病变（DR）是糖尿病常见并发症，可导致失明。早期检测至关重要，微动脉瘤点是最早的征兆，但因其微小且对比度低，识别轻度DR极具挑战。联邦学习（FL）能保护数据隐私，是医学图像处理的关键需求。它通过仅上传模型参数而非原始数据实现协作学习。受经典联邦学习启发，本文提出用于隐私保护的早期糖尿病视网膜病变检测的联邦量子神经网络（FQPDR）。模型在E-ophtha和Retina MNIST数据集上使用有限样本与少量可训练参数实现。在Kaggle数据集图像上的交叉评估显示，该系统具备良好的鲁棒性，性能优于现有非联邦与联邦方法。
原文摘要 · Abstract (English)
Diabetic Retinopathy (DR) is a common complication of diabetes that can lead to blindness of people. Detecting DR at the earliest stage is essential to prevent irreversible eye damage. Microaneurysm dots are the first signs of DR. As the dots are tiny and of low contrast, detecting mild DR is a very challenging task. Federated learning (FL) preserves data privacy, which is a major concern for medical image processing. FL is a collaborative learning method, which shares only the model parameters with a server, without sharing the patient data to a central server. Inspired by classical FL, we propose a federated learning-based quantum neural network (federated QNN) for this task. We implemented the models with limited samples and few learnable parameters from the E-ophtha and Retina MNIST datasets. The crossevaluation efficiency of the proposed federated quantum neural network system for privacy-preserving early detection of diabetic retinopathy (FQPDR) in Kaggle dataset images indicates the robustness of the light weight learning models. FQPDR performances are inspiring while considering existing non-FL and FL methods.
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