Tutorial: VAE as an inference paradigm for neuroimaging
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913649214357504 |
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| author | Vázquez-García, C. Martínez-Murcia, F. J. Román, F. Segovia Sáez, Juan M. Górriz |
| author_facet | Vázquez-García, C. Martínez-Murcia, F. J. Román, F. Segovia Sáez, Juan M. Górriz |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08009 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tutorial: VAE as an inference paradigm for neuroimaging Vázquez-García, C. Martínez-Murcia, F. J. Román, F. Segovia Sáez, Juan M. Górriz Image and Video Processing Artificial Intelligence 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. |
| title | Tutorial: VAE as an inference paradigm for neuroimaging |
| topic | Image and Video Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2501.08009 |