用量子增强特征提升骨折诊断速度与准确率
A Hybrid Quantum Classical Pipeline for X Ray Based Fracture Diagnosis
- 先降维后量子编码，融合经典与量子特征
- 16维特征向量分类准确率达99%，提速82%
- 适合医疗影像加速与低资源环境下的模型部署
骨骨折是全球导致残疾的主要原因，给医疗系统带来巨大负担。传统X射线解读耗时且易出错，现有机器学习方法常需大量特征工程、标注数据和高算力。为此，提出一种分布式混合量子-经典流程：先用主成分分析（PCA）降维，再通过4量子比特的量子幅值编码电路进行特征增强。将8个PCA特征与8个量子增强特征融合为16维向量，使用多种机器学习模型分类，在公开多区域X射线数据集上达到99%准确率，媲美顶尖迁移学习模型，同时将特征提取时间减少82%。
原文摘要 · Abstract (English)
Bone fractures are a leading cause of morbidity and disability worldwide, imposing significant clinical and economic burdens on healthcare systems. Traditional X ray interpretation is time consuming and error prone, while existing machine learning and deep learning solutions often demand extensive feature engineering, large, annotated datasets, and high computational resources. To address these challenges, a distributed hybrid quantum classical pipeline is proposed that first applies Principal Component Analysis (PCA) for dimensionality reduction and then leverages a 4 qubit quantum amplitude encoding circuit for feature enrichment. By fusing eight PCA derived features with eight quantum enhanced features into a 16 dimensional vector and then classifying with different machine learning models achieving 99% accuracy using a public multi region X ray dataset on par with state of the art transfer learning models while reducing feature extraction time by 82%.
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