量子与经典特征融合提升乳腺癌分类准确率
On the Complementarity of Quantum and Classical Features: Adaptive Hybrid Quantum-Classical Feature Fusion for Breast Cancer Classification
- 设计双分支架构，动态融合经典与量子特征
- 温度调制融合策略使准确率达87.82%，F1-score达91.77%
- 适合医疗图像分析与量子增强诊断系统研究者
将量子机器学习与经典深度学习结合，通过将数据映射到高维希尔伯特空间，为医学图像分析提供了新路径。然而，由于优化不对称性，统一两种范式仍具挑战。本文提出一种基于双分支特征提取的新型混合量子-经典架构，用于乳腺癌诊断。该框架从经典模型与量子电路中提取互补表征，探索可训练与确定性（非可训练）量子范式。为整合这些嵌入，提出三种渐进式特征融合策略：静态混合融合（SHF）用于离线提取，动态混合融合（DHF）支持端到端协同适应，以及一种新颖的温度调制混合融合（TSHF）。TSHF引入可学习标量，受多模态学习启发，动态平衡混合梯度动态并解决优化瓶颈。在BreastMNIST数据集上的实证验证表明，统一多样化特征表示可构建更丰富的数据上下文。当采用ResNet主干与可训练量子电路时，TSHF策略达到最高准确率87.82%、F1-score 91.77%、AUC-ROC 89.08%，优于纯经典基线。结果证明，该混合框架提升了分类准确率与阈值可靠性，为量子增强诊断工具的临床部署提供稳定高效架构。
原文摘要 · Abstract (English)
The integration of quantum machine learning with classical deep learning offers promising avenues for medical image analysis by mapping data into high-dimensional Hilbert spaces. However, effectively unifying these distinct paradigms remains challenging due to common optimization asymmetries. In this paper, a novel hybrid quantum-classical architecture for breast cancer diagnosis based on a dual-branch feature-extraction pipeline is proposed. Our framework extracts and unifies complementary representations from classical models and quantum circuits, exploring both trainable and deterministic (non-trainable) quantum paradigms. To integrate these embeddings, three progressive feature fusion strategies are introduced: Static Hybrid Fusion (SHF) for offline extraction, Dynamic Hybrid Fusion (DHF) for end-to-end co-adaptation, and a novel Temperature-Scaled Hybrid Fusion (TSHF). The TSHF strategy incorporates a learnable scalar, inspired by multimodal learning, that dynamically balances hybrid gradient dynamics and resolves optimization bottlenecks. Empirical validation on the BreastMNIST dataset confirms our hypothesis that unifying diverse feature representations creates a richer data context. The TSHF strategy, specifically when pairing a ResNet backbone with a trainable quantum circuit, achieved a peak accuracy of 87.82%, F1-score of 91.77%, and an AUC-ROC of 89.08%, outperforming purely classical baselines. These results demonstrate that the proposed hybrid framework improves classification accuracy and threshold reliability, providing a stable, high-performance architecture for the clinical deployment of quantum-enhanced diagnostic tools.
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