混合量子经典网络提升医学图像分类准确率
HQCNN: A Hybrid Quantum-Classical Neural Network for Medical Image Classification
- 用5层卷积+4量子比特可变电路融合量子态编码与注意力机制
- 在6个数据集上最高达99.95%准确率，噪声数据下仍保持87.18%精度
- 参数少、泛化强，适合标注数据少的医学场景
医学图像分类在医疗影像分析中至关重要，但受限于标注数据少、类别不平衡及医学模式复杂，仍具挑战。为此，我们提出一种新型混合量子-经典神经网络（HQCNN），用于二分类和多分类任务。该架构结合五层经典卷积主干与四量子比特可变量子电路，集成量子态编码、叠加纠缠与傅里叶启发的量子注意力机制。我们在六个MedMNIST v2基准数据集上评估模型，结果表明HQCNN持续优于经典与量子基线，在PathMNIST（二分类）上达到最高99.91%准确率与100.00% AUC，OrganAMNIST（多分类）达99.95%准确率，且在噪声数据集BreastMNIST上仍保持87.18%准确率。模型展现出优异的泛化能力与计算效率，仅需极少可训练参数，适用于数据稀缺场景。研究结果为混合量子-经典模型推动医学影像任务提供了有力实证。
原文摘要 · Abstract (English)
Classification of medical images plays a vital role in medical image analysis; however, it remains challenging due to the limited availability of labeled data, class imbalances, and the complexity of medical patterns. To overcome these challenges, we propose a novel Hybrid Quantum-Classical Neural Network (HQCNN) for both binary and multi-class classification. The architecture of HQCNN integrates a five-layer classical convolutional backbone with a 4-qubit variational quantum circuit that incorporates quantum state encoding, superpositional entanglement, and a Fourier-inspired quantum attention mechanism. We evaluate the model on six MedMNIST v2 benchmark datasets. The HQCNN consistently outperforms classical and quantum baselines, achieving up to 99.91% accuracy and 100.00% AUC on PathMNIST (binary) and 99.95% accuracy on OrganAMNIST (multi-class) with strong robustness on noisy datasets like BreastMNIST (87.18% accuracy). The model demonstrates superior generalization capability and computational efficiency, accomplished with significantly fewer trainable parameters, making it suitable for data-scarce scenarios. Our findings provide strong empirical evidence that hybrid quantum-classical models can advance medical imaging tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。