用变分自编码器解析脑影像数据，挖掘神经退行性病变模式
Tutorial: VAE as an inference paradigm for neuroimaging
- 结合深度学习与贝叶斯推断，生成可解释的低维隐变量表示
- 解决收敛困难与过拟合问题，采用重参数化技巧优化训练稳定性
- 适合脑科学、医学影像分析者，尤其关注疾病机制探索的研究者
本教程探讨变分自编码器（VAEs）这一无监督学习的核心框架，特别适用于高维脑影像数据。通过融合深度学习与贝叶斯推断，VAEs 能生成具有可解释性的潜在表示。教程系统阐述了 VAEs 的理论基础，讨论了收敛性问题与过拟合等实际挑战，并介绍了重参数化技巧与超参数优化策略。同时，重点展示了 VAE 在脑影像中的关键应用，证明其在揭示神经退行性病变相关模式方面的潜力，为复杂脑数据的分析提供新范式。
原文摘要 · Abstract (English)
In this tutorial, we explore Variational Autoencoders (VAEs), an essential framework for unsupervised learning, particularly suited for high-dimensional datasets such as neuroimaging. By integrating deep learning with Bayesian inference, VAEs enable the generation of interpretable latent representations. This tutorial outlines the theoretical foundations of VAEs, addresses practical challenges such as convergence issues and over-fitting, and discusses strategies like the reparameterization trick and hyperparameter optimization. We also highlight key applications of VAEs in neuroimaging, demonstrating their potential to uncover meaningful patterns, including those associated with neurodegenerative processes, and their broader implications for analyzing complex brain data.
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