🤖 AI Summary
This work proposes a lightweight classical-quantum hybrid architecture to address key challenges in medical image analysis, including scarce labeled data, limited computational resources, and poor model generalization. Building upon a MobileNetV2 backbone, the framework leverages SimCLR-style self-supervised contrastive pretraining to learn generalizable representations and integrates a low-parameter parametrized quantum circuit (PQC) module to enhance feature discriminability. To the best of our knowledge, this is the first approach to combine self-supervised learning with lightweight quantum modeling. With only 2–3 million parameters, the model achieves significant performance gains over classical baselines after fine-tuning on small annotated datasets, consistently improving accuracy, AUC, and F1-score. Feature visualizations further confirm the discriminative power and stability of the learned representations.
📝 Abstract
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.