Decoodt Pierre, Liang Tan Jun, Bopardikar Soham, Santhanam Hemavathi, Eyembe Alfaxad, Garcia-Zapirain Begonya, Sierra-Sosa Daniel
Cardiologie, Centre Hospitalo-Universitaire Brugmann, Faculté de Médecine, Université Libre de Bruxelles, Place Van Gehuchten 4, 1020 Brussels, Belgium.
School of Computer Science, Digital Health and Innovations Impact Lab, Taylor's University, Subang Jaya 47500, Selangor, Malaysia.
J Imaging. 2023 Jun 25;9(7):128. doi: 10.3390/jimaging9070128.
Cardiovascular diseases are among the major health problems that are likely to benefit from promising developments in quantum machine learning for medical imaging. The chest X-ray (CXR), a widely used modality, can reveal cardiomegaly, even when performed primarily for a non-cardiological indication. Based on pre-trained DenseNet-121, we designed hybrid classical-quantum (CQ) transfer learning models to detect cardiomegaly in CXRs. Using Qiskit and PennyLane, we integrated a parameterized quantum circuit into a classic network implemented in PyTorch. We mined the CheXpert public repository to create a balanced dataset with 2436 posteroanterior CXRs from different patients distributed between cardiomegaly and the control. Using k-fold cross-validation, the CQ models were trained using a state vector simulator. The normalized global effective dimension allowed us to compare the trainability in the CQ models run on Qiskit. For prediction, ROC AUC scores up to 0.93 and accuracies up to 0.87 were achieved for several CQ models, rivaling the classical-classical (CC) model used as a reference. A trustworthy Grad-CAM++ heatmap with a hot zone covering the heart was visualized more often with the QC option than that with the CC option (94% vs. 61%, < 0.001), which may boost the rate of acceptance by health professionals.
心血管疾病是主要的健康问题之一，有望从医学成像量子机器学习的蓬勃发展中受益。胸部X光（CXR）是一种广泛使用的检查方式，即使主要是为了非心脏科指征进行检查，也能显示出心脏肥大。基于预训练的DenseNet-121，我们设计了混合经典-量子（CQ）迁移学习模型来检测胸部X光片中的心脏肥大。使用Qiskit和PennyLane，我们将一个参数化量子电路集成到了用PyTorch实现的经典网络中。我们挖掘了CheXpert公共存储库，以创建一个平衡数据集，其中包含来自不同患者的2436张后前位胸部X光片，这些片子分布在心脏肥大组和对照组之间。使用k折交叉验证，通过状态向量模拟器对CQ模型进行训练。归一化全局有效维度使我们能够比较在Qiskit上运行的CQ模型的可训练性。在预测方面，几个CQ模型的ROC AUC分数高达0.93，准确率高达0.87，可与用作参考的经典-经典（CC）模型相媲美。与CC选项相比，QC选项更常可视化出一个覆盖心脏的热区的可信Grad-CAM++热图（94%对61%，<0.001），这可能会提高健康专业人员的接受率。
