基于BI-Mamba的多视图胸部X光片心血管疾病检测
Cardiovascular Disease Detection from Multi-View Chest X-rays with BI-Mamba
AI总结：
针对CT辐射风险，提出BI-Mamba模型利用双向状态空间建模多视图胸部X光片，在NLST数据集上优于ResNet-50和ViT-S，并节省GPU内存，展现胸部X光预测CVD风险的潜力。
摘要：
在医学影像中准确预测心血管疾病（CVD）风险对于有效的患者健康管理至关重要。先前的研究表明，计算机断层扫描（CT）中的影像特征有助于预测CVD风险。然而，CT涉及显著的辐射暴露，可能对患者健康产生不良影响。相比之下，胸部X光片发射的辐射水平显著较低，提供了更安全的选择。这一理由促使我们研究使用胸部X光片预测CVD风险的可行性。卷积神经网络（CNN）和Transformer是用于计算机辅助诊断的两种成熟网络架构。然而，由于缺乏大范围上下文建模能力或具有二次时间复杂度，它们难以处理超高分辨率的胸部X光片。受状态空间序列模型（SSM）的启发，这是一类具有与Transformer相当的序列建模能力和线性时间复杂度的新型网络架构，我们提出了双向图像Mamba（BI-Mamba），以用相反方向的信息补充单向SSM。BI-Mamba利用并行的前向和后向块来编码多视图胸部X光片的长程依赖性。我们在国家肺癌筛查试验（NLST）中来自10,395名受试者的图像上进行了广泛实验。结果表明，BI-Mamba在参数规模相当的情况下优于ResNet-50和ViT-S，并在训练期间节省了大量GPU内存。此外，与先前在CT上的最新技术相比，BI-Mamba取得了有前景的性能，揭示了胸部X光片在CVD风险预测中的潜力。
英文摘要：
Accurate prediction of Cardiovascular disease (CVD) risk in medical imaging is central to effective patient health management. Previous studies have demonstrated that imaging features in computed tomography (CT) can help predict CVD risk. However, CT entails notable radiation exposure, which may result in adverse health effects for patients. In contrast, chest X-ray emits significantly lower levels of radiation, offering a safer option. This rationale motivates our investigation into the feasibility of using chest X-ray for predicting CVD risk. Convolutional Neural Networks (CNNs) and Transformers are two established network architectures for computer-aided diagnosis. However, they struggle to model very high resolution chest X-ray due to the lack of large context modeling power or quadratic time complexity. Inspired by state space sequence models (SSMs), a new class of network architectures with competitive sequence modeling power as Transfomers and linear time complexity, we propose Bidirectional Image Mamba (BI-Mamba) to complement the unidirectional SSMs with opposite directional information. BI-Mamba utilizes parallel forward and backwark blocks to encode longe-range dependencies of multi-view chest X-rays. We conduct extensive experiments on images from 10,395 subjects in National Lung Screening Trail (NLST). Results show that BI-Mamba outperforms ResNet-50 and ViT-S with comparable parameter size, and saves significant amount of GPU memory during training. Besides, BI-Mamba achieves promising performance compared with previous state of the art in CT, unraveling the potential of chest X-ray for CVD risk prediction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
