轻量级医学图像分类框架，结合自监督与量子特征增强。
A Lightweight Medical Image Classification Framework via Self-Supervised Contrastive Learning and Quantum-Enhanced Feature Modeling
- 用自监督对比学习预训练小型网络，减少标注依赖。
- 仅200万参数，准确率、AUC等指标优于传统方法。
- 适合算力有限但需高精度的医疗AI场景。
智能医学图像分析对临床决策支持至关重要，但常受限于标注数据稀缺、计算资源不足及模型泛化能力差。为此，本文提出一种轻量级医学图像分类框架，融合自监督对比学习与量子增强特征建模。采用MobileNetV2作为紧凑主干网络，在无标签图像上通过类似SimCLR的自监督方式预训练。嵌入轻量级参数化量子电路（PQC）作为量子特征增强模块，构建混合经典-量子架构，并在少量标注数据上微调。实验表明，该方法仅需约200万参数、计算成本低，但在准确率、AUC和F1-score上持续优于未使用自监督或量子增强的基线模型。特征可视化显示其具备更强判别性与表示稳定性。整体为资源受限环境下高性能医疗AI提供了实用且前瞻性的解决方案。
原文摘要 · Abstract (English)
Intelligent medical image analysis is essential for clinical decision support but is often limited by scarce annotations, constrained computational resources, and suboptimal model generalization. To address these challenges, we propose a lightweight medical image classification framework that integrates self-supervised contrastive learning with quantum-enhanced feature modeling. MobileNetV2 is employed as a compact backbone and pretrained using a SimCLR-style self-supervised paradigm on unlabeled images. A lightweight parameterized quantum circuit (PQC) is embedded as a quantum feature enhancement module, forming a hybrid classical-quantum architecture, which is subsequently fine-tuned on limited labeled data. Experimental results demonstrate that, with only approximately 2-3 million parameters and low computational cost, the proposed method consistently outperforms classical baselines without self-supervised learning or quantum enhancement in terms of Accuracy, AUC, and F1-score. Feature visualization further indicates improved discriminability and representation stability. Overall, this work provides a practical and forward-looking solution for high-performance medical artificial intelligence under resource-constrained settings.
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