量子混合模型提升心脏超声图像视角识别准确率
QuantumBoostNet: Hybrid Classical-Quantum Cardiac View Identification
- 融合经典网络与10量子比特电路的双头架构
- 在多个数据集上显著优于传统模型，尤其抗噪性强
- 适合医疗影像中高噪声场景下的视觉分析任务
准确识别心脏超声（心电图）中的正确视角或角度对心脏病学影像、精确解剖解读及减少临床错误至关重要。现有主流经典模型在标准基准上表现良好，但在高噪声的专业医学影像中效果不佳。为此，本文提出混合经典-量子架构QuantumBoostNet，结合经典主干网络与两个分支：一个经典分支和一个含10个量子比特的参数化量子电路分支。主要贡献在于两阶段训练策略，通过监控损失动态的混合参数自适应切换分支。大量实验表明，QuantumBoostNet在相同训练条件下优于基线模型，在FashionMNIST（p_t=9.91×10⁻⁶）和MNIST（p_t=1.29×10⁻⁵）上取得统计显著提升，心电图任务上提升不显著（p_t=0.0711），但整体对噪声表现出强鲁棒性。结果支持继续发展混合经典-量子模型用于专业医学影像应用。
原文摘要 · Abstract (English)
Accurate identification of the correct view or angle in cardiac ultrasound (echocardiogram) is critical for cardiologic imaging, precise anatomical interpretation, and reducing clinical errors. Most state-of-the-art classical models perform well on standard benchmarks but give suboptimal results in specialized medical imaging due to high noise levels. To address these challenges, this work proposes the hybrid classical-quantum architecture QuantumBoostNet, which combines a classical backbone with two heads: one classical and one quantum, a parametrized 10-qubit quantum circuit. The main contribution of this work is training in two stages, with an adaptive transition between heads controlled by a mixing parameter that monitors loss dynamics. Extensive experiments show that QuantumBoostNet outperforms the implemented baselines under matched training conditions. Statistically significant gains appear on FashionMNIST ($p_t=9.91\!\times\!10^{-6}$) and MNIST ($p_t=1.29\!\times\!10^{-5}$), with a less significant improvement on the echocardiography task ($p_t=0.0711$). The model also shows robustness to noise. These findings support continued development of hybrid classical-quantum models for specialized medical imaging applications.
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