# Variational AutoEncoder For Regression: Application to Brain Aging Analysis

arXiv: https://arxiv.org/abs/1904.05948 · HTML: https://mlsanity.com/paper/1904.05948/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2019.

## Built on by (most cited first, 30 of 30)

- [Learning Disentangled Representations in the Imaging Domain](https://mlsanity.com/paper/2108.12043/) (2021)
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- [Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction](https://mlsanity.com/paper/2201.13081/) (2022)
- [Semi-Supervised Diffusion Model for Brain Age Prediction](https://mlsanity.com/paper/2402.09137/) (2024)
- [Material Microstructure Design Using VAE-Regression with Multimodal Prior](https://mlsanity.com/paper/2402.17806/) (2024)
- [Applications of interpretable deep learning in neuroimaging: a comprehensive review](https://mlsanity.com/paper/2406.17792/) (2024)
- [IdenBAT: Disentangled Representation Learning for Identity-Preserved Brain Age Transformation](https://mlsanity.com/paper/2410.16945/) (2024)
- [Disentangled representations: towards interpretation of sex determination from hip bone](https://mlsanity.com/paper/2112.09414/) (2021)
- [AI-based association analysis for medical imaging using latent-space geometric confounder correction](https://mlsanity.com/paper/2311.12836/) (2023)
- [Identity Preserving Latent Diffusion for Brain Aging Modeling](https://mlsanity.com/paper/2503.09634/) (2025)
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- [DiffoR: A Unified Continuous Generative Framework for Universal Ordinal Regression](https://mlsanity.com/paper/2606.07599/) (2026)
- [Detection of Dataset Shifts in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression](https://mlsanity.com/paper/2104.06613/) (2021)
- [A Generative Adversarial Network-based Selective Ensemble Characteristic-to-Expression Synthesis (SE-CTES) Approach and Its Applications in Healthcare](https://mlsanity.com/paper/2205.14751/) (2022)
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- [Semi-supervised Variational Autoencoder for Regression: Application on Soft Sensors](https://mlsanity.com/paper/2211.05979/) (2022)
- [A Lightweight Generative Model for Interpretable Subject-level Prediction](https://mlsanity.com/paper/2306.11107/) (2023)
- [Deep Learning Framework for Spleen Volume Estimation from 2D Cross-sectional Views](https://mlsanity.com/paper/2308.08038/) (2023)
- [MoFormer: Multi-objective Antimicrobial Peptide Generation Based on Conditional Transformer Joint Multi-modal Fusion Descriptor](https://mlsanity.com/paper/2406.02610/) (2024)
- [Taylor-Sensus Network: Embracing Noise to Enlighten Uncertainty for Scientific Data](https://mlsanity.com/paper/2409.07942/) (2024)
- [Statistical testing on generative AI anomaly detection tools in Alzheimer's Disease diagnosis](https://mlsanity.com/paper/2410.13363/) (2024)
- [DeepSPV: A Deep Learning Pipeline for 3D Spleen Volume Estimation from 2D Ultrasound Images](https://mlsanity.com/paper/2411.11190/) (2024)
- [Sequence models for continuous cell cycle stage prediction from brightfield images](https://mlsanity.com/paper/2502.02182/) (2025)
- [MRExtrap: Longitudinal Aging of Brain MRIs using Linear Modeling in Latent Space](https://mlsanity.com/paper/2508.19482/) (2025)
- [An explainable framework for the relationship between dementia and glucose metabolism patterns](https://mlsanity.com/paper/2601.20480/) (2026)
- [Orthogonal Dendritic Intrinsic Networks: An Architecture for Significance-Ordered, Orthogonal Latent Spaces](https://mlsanity.com/paper/2607.05653/) (2026)

## Abstract (arXiv)

While unsupervised variational autoencoders (VAE) have become a powerful tool in neuroimage analysis, their application to supervised learning is under-explored. We aim to close this gap by proposing a unified probabilistic model for learning the latent space of imaging data and performing supervised regression. Based on recent advances in learning disentangled representations, the novel generative process explicitly models the conditional distribution of latent representations with respect to the regression target variable. Performing a variational inference procedure on this model leads to joint regularization between the VAE and a neural-network regressor. In predicting the age of 245 subjects from their structural Magnetic Resonance (MR) images, our model is more accurate than state-of-the-art methods when applied to either region-of-interest (ROI) measurements or raw 3D volume images. More importantly, unlike simple feed-forward neural-networks, disentanglement of age in latent representations allows for intuitive interpretation of the structural developmental patterns of the human brain.