用量子卷积网络集成提升乳腺癌病理图像分类准确率
An ensemble framework approach of hybrid Quantum convolutional neural networks for classification of breast cancer images
- 融合三种混合量子卷积网络，通过集成学习增强分类性能
- 集成后准确率达86.72%，优于单个模型及经典网络
- 适合关注量子机器学习在医疗影像中应用的研究者
量子神经网络因其利用量子叠加和纠缠等独特现象，在学习能力和模型扩展性方面被认为可替代经典神经网络。然而，在当前的噪声中等规模量子（NISQ）时代，量子模型的可训练性和表达能力仍需深入研究。医学图像分类则广泛适用于深度学习，尤其是卷积神经网络。本文研究了三种混合经典-量子神经网络架构，并在乳腺癌组织病理学数据集上使用标准集成技术进行组合。单个模型最高准确率为85.59%，而集成后准确率提升至86.72%，优于单一混合网络及对应的经典神经网络模型。
原文摘要 · Abstract (English)
Quantum neural networks are deemed suitable to replace classical neural networks in their ability to learn and scale up network models using quantum-exclusive phenomena like superposition and entanglement. However, in the noisy intermediate scale quantum (NISQ) era, the trainability and expressibility of quantum models are yet under investigation. Medical image classification on the other hand, pertains well to applications in deep learning, particularly, convolutional neural networks. In this paper, we carry out a study of three hybrid classical-quantum neural network architectures and combine them using standard ensembling techniques on a breast cancer histopathological dataset. The best accuracy percentage obtained by an individual model is 85.59. Whereas, on performing ensemble, we have obtained accuracy as high as 86.72%, an improvement over the individual hybrid network as well as classical neural network counterparts of the hybrid network models.
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