通过自监督对比学习和量子增强特征建模的轻量级医学图像分类框架
A Lightweight Medical Image Classification Framework via Self-Supervised Contrastive Learning and Quantum-Enhanced Feature Modeling
AI总结：
本文提出一种结合自监督对比学习和量子增强特征建模的轻量级医学图像分类框架，通过MobileNetV2和量子电路实现高效模型，实验表明其在性能上优于传统方法。
摘要：
智能医学图像分析对于临床决策支持至关重要，但往往受限于标注稀缺、计算资源受限和模型泛化不足。为解决这些挑战，我们提出了一种轻量级医学图像分类框架，整合了自监督对比学习与量子增强特征建模。MobileNetV2被用作紧凑的主干网络，并通过SimCLR风格的自监督范式在未标注图像上预训练。一个轻量级参数化量子电路（PQC）被嵌入作为量子特征增强模块，形成混合的经典-量子架构，随后在有限的标注数据上进行微调。实验结果表明，仅约2-3百万参数和低计算成本的该方法在准确率、AUC和F1分数方面始终优于没有自监督学习或量子增强的经典基线。特征可视化进一步表明了改进的判别性和表示稳定性。总体而言，这项工作为资源受限设置下的高性能医学人工智能提供了一个实用且前瞻性的解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
