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.

翻译：医学图像人工智能模型通常在单中心或单设备环境下表现出优异性能，然而在实际跨中心部署中，由于领域偏移的存在，其效果常显著下降，限制了临床泛化能力。为应对这一挑战，我们提出一种结合量子增强协同学习的轻量化领域泛化框架，能够在无需真实多中心标注数据的情况下，实现对未见目标领域的鲁棒泛化。具体而言，我们构建了基于MobileNetV2的领域不变编码器，并通过三个核心组件进行优化：（1）利用亮度、对比度、锐化及噪声扰动进行多领域成像偏移模拟，以复现异构采集条件；（2）采用梯度反转的领域对抗训练以抑制领域判别性特征；（3）设计轻量化量子特征增强层，通过参数化量子电路实现非线性特征映射与纠缠建模。此外，在推理阶段采用测试时自适应策略以进一步缓解分布偏移。在模拟多中心医学影像数据集上的实验表明，所提方法在未见领域上显著优于未使用领域泛化或量子增强的基线模型，实现了更低的领域特异性性能方差，并提升了AUC与敏感度。这些结果凸显了在有限计算资源下量子增强领域泛化的临床潜力，为混合量子-经典医学影像系统提供了可行范式。