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

arXiv: https://arxiv.org/abs/2601.16608 · HTML: https://mlsanity.com/paper/2601.16608/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 23 Jan 2026.

## Builds on (most cited first, 3 of 3)

- [Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses](https://mlsanity.com/paper/2509.10620/) (2025)
- [RS-MOCO: A deep learning-based topology-preserving image registration method for cardiac T1 mapping](https://mlsanity.com/paper/2410.11651/) (2024)
- [Investigating Quantum Feature Maps in Quantum Support Vector Machines for Lung Cancer Classification](https://mlsanity.com/paper/2506.03272/) (2025)

## Built on by (most cited first, 1 of 1)

- [Generative Diffusion Augmentation with Quantum-Enhanced Discrimination for Medical Image Diagnosis](https://mlsanity.com/paper/2601.18556/) (2026)

## Abstract (arXiv)

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.