Integrative Variational Autoencoders for Generative Modeling of an Image Outcome with Multiple Input Images

• URL: http://arxiv.org/abs/2402.02734v2
• Date: Fri, 12 Sep 2025 05:11:48 GMT
• Title: Integrative Variational Autoencoders for Generative Modeling of an Image Outcome with Multiple Input Images
• Authors: Bowen Lei, Yeseul Jeon, Rajarshi Guhaniyogi, Aaron Scheffler, Bani Mallick, Alzheimer's Disease Neuroimaging Initiatives,
• Abstract summary: We introduce the Integrative Variational Autoencoder (InVA), the first hierarchical VAE framework for image-on-image regression in neuroimaging.<n>InVA accurately predicts costly PET scans from structural MRI, offering an efficient and powerful tool for multimodal neuroimaging.
• Score: 3.344876046963058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding relationships across multiple imaging modalities is central to neuroimaging research. We introduce the Integrative Variational Autoencoder (InVA), the first hierarchical VAE framework for image-on-image regression in multimodal neuroimaging. Unlike standard VAEs, which are not designed for predictive integration across modalities, InVA models outcome images as functions of both shared and modality-specific features. This flexible, data-driven approach avoids rigid assumptions of classical tensor regression and outperforms conventional VAEs and nonlinear models such as BART. As a key application, InVA accurately predicts costly PET scans from structural MRI, offering an efficient and powerful tool for multimodal neuroimaging.

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