混合古典-量子转移学习用于胸部X射线中心肌大病检测
Pierre Decoodt1, Tan Jun Liang2,3, Soham Bopardikar4
1Cardiologie, Centre Hospitalo-Universitaire Brugmann, Faculté de Médecine, Université Libre de Bruxelles, Place Van Gehuchten 4, 1020 Brussels, Belgium.
Journal of imaging
|July 28, 2023
概括
量子机器学习模型显示,在胸部X射线 (CXR) 中检测心脏病 (心脏膨胀) 的可能性很大. 这些混合经典-量子模型实现了高精度,与传统方法相竞争,并为医疗保健专业人员改善了可视化.
科学领域:
- 量子计算在医疗保健中的应用.
- 机器学习用于医学图像分析.
- 心血管疾病诊断 心血管疾病诊断
背景情况:
- 心血管疾病对健康构成重大挑战.
- 胸部X射线 (CXR) 对于诊断诸如心壮病等疾病至关重要.
- 量子机器学习 (QML) 为医学成像分析提供了潜在的进步.
研究的目的:
- 开发和评估混合古典-量子 (CQ) 转移学习模型,用于检测CXRs中的心脏病.
- 为了比较CQ模型与古典-古典 (CC) 模型的性能.
- 用热图来评估CQ模型的可解释性.
主要方法:
- 设计了混合CQ转移学习模型,将参数化量子电路 (使用Qiskit和PennyLane) 与经典网络 (PyTorch) 集成在一起.
- 利用来自CheXpert存储库的2436个前后CXR的平衡数据集.
- 采用k-fold交叉验证和状态向量模拟器来训练CQ模型.
- 使用规范化的全球有效维度分析了可训练性.
主要成果:
- 实现了高预测性能,ROC AUC得分高达0.93和精度高达0.87,与CC模型相比.
- 与CC模型 (61%) 相比,使用CQ模型 (94%) 显示可信的Grad-CAM++热图覆盖心脏的可视化频率明显增加.
结论:
- 混合古典-量子模型显示出在CXR中准确检测心肌大病的巨大潜力.
- 提高CQ模型的可解释性可能会增加医疗保健专业人员对其的采用.
- QML代表了通过医学成像改善心血管诊断的一个有希望的前沿.
关键词:
心脏病巨大的心脏病.心血管疾病心血管疾病胸部X射线 胸部X射线诊断 诊断 诊断 诊断 诊断 诊断心脏衰竭是因为心脏衰竭.机器学习是机器学习.医学成像医学成像量子计算是一种量子计算.转移学习转移学习视觉化的可视化更多相关视频
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