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.

翻译：智能医学图像分析对于临床决策支持至关重要，但常受限于标注稀缺、计算资源受限以及模型泛化能力不足。为应对这些挑战，我们提出一种轻量化医学图像分类框架，该框架将自监督对比学习与量子增强特征建模相结合。我们采用MobileNetV2作为紧凑骨干网络，并利用SimCLR风格的自监督范式在未标注图像上进行预训练。一个轻量化的参数化量子电路被嵌入作为量子特征增强模块，构成一种经典-量子混合架构，随后在有限标注数据上进行微调。实验结果表明，仅使用约2-3百万参数和较低计算成本，所提方法在准确率、AUC和F1分数上均持续优于未采用自监督学习或量子增强的经典基线方法。特征可视化进一步表明其判别能力和表示稳定性得到提升。总体而言，这项工作为资源受限环境下实现高性能医学人工智能提供了一种实用且具有前瞻性的解决方案。