# Domain Generalization with Quantum Enhancement for Medical Image Classification: A Lightweight Approach for Cross-Center Deployment

arXiv: https://arxiv.org/abs/2601.17862 · HTML: https://mlsanity.com/paper/2601.17862/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 25 Jan 2026.

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

- [Large-Vocabulary Segmentation for Medical Images with Text Prompts](https://mlsanity.com/paper/2312.17183/) (2023)
- [A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis](https://mlsanity.com/paper/2505.14715/) (2025)
- [Style Content Decomposition-based Data Augmentation for Domain Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2502.20619/) (2025)

## Abstract (arXiv)

Medical image artificial intelligence models often achieve strong performance in single-center or single-device settings, yet their effectiveness frequently deteriorates in real-world cross-center deployment due to domain shift, limiting clinical generalizability. To address this challenge, we propose a lightweight domain generalization framework with quantum-enhanced collaborative learning, enabling robust generalization to unseen target domains without relying on real multi-center labeled data. Specifically, a MobileNetV2-based domain-invariant encoder is constructed and optimized through three key components: (1) multi-domain imaging shift simulation using brightness, contrast, sharpening, and noise perturbations to emulate heterogeneous acquisition conditions; (2) domain-adversarial training with gradient reversal to suppress domain-discriminative features; and (3) a lightweight quantum feature enhancement layer that applies parameterized quantum circuits for nonlinear feature mapping and entanglement modeling. In addition, a test-time adaptation strategy is employed during inference to further alleviate distribution shifts. Experiments on simulated multi-center medical imaging datasets demonstrate that the proposed method significantly outperforms baseline models without domain generalization or quantum enhancement on unseen domains, achieving reduced domain-specific performance variance and improved AUC and sensitivity. These results highlight the clinical potential of quantum-enhanced domain generalization under constrained computational resources and provide a feasible paradigm for hybrid quantum--classical medical imaging systems.