A Lightweight Medical Image Classification Framework via Self-Supervised Contrastive Learning and Quantum-Enhanced Feature Modeling
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.