用图卷积变分自编码器生成逼真腹主动脉瘤数据，解决小样本隐私问题。
Graph-Convolutional-Beta-VAE for Synthetic Abdominal Aorta Aneurysm Generation
- 结合图卷积与变分自编码器，从少量真实数据中学习解耦的解剖特征。
- 生成数据在保持解剖结构完整性的前提下，多样性提升且真实感强。
- 适合医学影像研究、医疗器械测试及计算建模，尤其适用于数据稀缺场景。
合成数据生成在医学研究中至关重要，可缓解隐私问题并支持大规模患者数据分析。本研究提出一种基于图卷积神经网络的β-变分自编码器框架，用于生成腹主动脉瘤（AAA）的合成数据。利用小规模真实数据集，该方法提取关键解剖特征，并在紧凑的解耦潜在空间中捕捉复杂的统计关系。为应对数据量不足，采用基于Procrustes分析的低影响数据增强策略，有效保留解剖完整性。生成策略包含确定性和随机性两种方式，在提升数据多样性的同时确保生成结果的真实性。相较于基于PCA的方法，本模型在未见数据上表现更稳健，能更好地捕捉非线性解剖变异。生成的合成AAA数据集既保护患者隐私，又为临床与统计分析、器械测试及计算建模提供可扩展基础。
原文摘要 · Abstract (English)
Synthetic data generation plays a crucial role in medical research by mitigating privacy concerns and enabling large-scale patient data analysis. This study presents a beta-Variational Autoencoder Graph Convolutional Neural Network framework for generating synthetic Abdominal Aorta Aneurysms (AAA). Using a small real-world dataset, our approach extracts key anatomical features and captures complex statistical relationships within a compact disentangled latent space. To address data limitations, low-impact data augmentation based on Procrustes analysis was employed, preserving anatomical integrity. The generation strategies, both deterministic and stochastic, manage to enhance data diversity while ensuring realism. Compared to PCA-based approaches, our model performs more robustly on unseen data by capturing complex, nonlinear anatomical variations. This enables more comprehensive clinical and statistical analyses than the original dataset alone. The resulting synthetic AAA dataset preserves patient privacy while providing a scalable foundation for medical research, device testing, and computational modeling.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。