用量子卷积网络结合生成数据，97.5%准确率检测阿尔茨海默病。
CQ CNN: A Hybrid Classical Quantum Convolutional Neural Network for Alzheimer's Disease Detection Using Diffusion Generated and U Net Segmented 3D MRI
- 混合经典量子卷积架构，用扩散模型生成少数类脑影像数据。
- 仅13K参数达97.5%准确率，比现有最优模型少99.99%参数。
- 适合数据少、算力弱的临床场景，首次实现量子医疗影像生成。
基于临床3D MRI数据的阿尔茨海默病（AD）检测是医学影像领域的研究热点。本文提出一种端到端的混合经典-量子卷积神经网络（CQ CNN），用于AD检测。方法包括构建3D MRI数据可用性框架、设计并训练脑组织分割模型Skull Net，以及训练扩散模型生成少数类合成图像。实验表明，收敛后的模型展现出潜在量子优势，在更少轮次内达到更高准确率。所提beta8三量子比特模型准确率达97.50%，超越当前最先进（SOTA）模型，且仅需13K参数（0.48 MB），相比现有SOTA模型减少超过99.99%。此外，用于训练量子模型的扩散生成数据与真实样本结合，保持临床结构标准，为量子机器学习领域首次实现。结论认为，经梯度优化改进后，此类CQCNN架构有望成为资源受限临床环境下的可行甚至替代性方案。
原文摘要 · Abstract (English)
The detection of Alzheimer disease (AD) from clinical MRI data is an active area of research in medical imaging. Recent advances in quantum computing, particularly the integration of parameterized quantum circuits (PQCs) with classical machine learning architectures, offer new opportunities to develop models that may outperform traditional methods. However, quantum machine learning (QML) remains in its early stages and requires further experimental analysis to better understand its behavior and limitations. In this paper, we propose an end to end hybrid classical quantum convolutional neural network (CQ CNN) for AD detection using clinically formatted 3D MRI data. Our approach involves developing a framework to make 3D MRI data usable for machine learning, designing and training a brain tissue segmentation model (Skull Net), and training a diffusion model to generate synthetic images for the minority class. Our converged models exhibit potential quantum advantages, achieving higher accuracy in fewer epochs than classical models. The proposed beta8 3 qubit model achieves an accuracy of 97.50%, surpassing state of the art (SOTA) models while requiring significantly fewer computational resources. In particular, the architecture employs only 13K parameters (0.48 MB), reducing the parameter count by more than 99.99% compared to current SOTA models. Furthermore, the diffusion-generated data used to train our quantum models, in conjunction with real samples, preserve clinical structural standards, representing a notable first in the field of QML. We conclude that CQCNN architecture like models, with further improvements in gradient optimization techniques, could become a viable option and even a potential alternative to classical models for AD detection, especially in data limited and resource constrained clinical settings.
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