ArticleBiometrics2022
A spatial Bayesian latent factor model for image-on-image regression.
Article in Biometrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- MULTI-OBJECT DATA INTEGRATION IN THE STUDY OF PRIMARY PROGRESSIVE APHASIA.The annals of applied statistics · 2025Article
- Covariance-on-covariance regression.Biometrics · 2025Article
- Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process.Journal of the American Statistical Association · 2024Article
- Mediation Analysis for High-Dimensional Mediators and Outcomes with an Application to Multimodal Imaging Data.Computational statistics & data analysis · 2023Article
- Identifying regions of interest in mammogram images.Statistical methods in medical research · 2023Article
- Discussion of "LESA: Longitudinal Elastic Shape Analysis of Brain Subcortical Structures".Journal of the American Statistical Association · 2023Article
- The normative modeling framework for computational psychiatry.Nature protocols · 2022Review
- DADP: Dynamic abnormality detection and progression for longitudinal knee magnetic resonance images from the Osteoarthritis Initiative.Medical image analysis · 2022Article
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Authors and funding
3 authors.
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Abstract
Image-on-image regression analysis, using images to predict images, is a challenging task, due to (1) the high dimensionality and (2) the complex spatial dependence structures in image predictors and image outcomes. In this work, we propose a novel image-on-image regression model, by extending a spatial Bayesian latent factor model to image data, where low-dimensional latent factors are adopted to make connections between high-dimensional image outcomes and image predictors. We assign Gaussian process priors to the spatially varying regression coefficients in the model, which can well capture the complex spatial dependence among image outcomes as well as that among the image predictors. We perform simulation studies to evaluate the out-of-sample prediction performance of our method compared with linear regression and voxel-wise regression methods for different scenarios. The proposed method achieves better prediction accuracy by effectively accounting for the spatial dependence and efficiently reduces image dimensions with latent factors. We apply the proposed method to analysis of multimodal image data in the Human Connectome Project where we predict task-related contrast maps using subcortical volumetric seed maps.
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