Latent Representation Learning from 3D Brain MRI for Interpretable Prediction in Multiple Sclerosis
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916981593079808 |
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| author | Huynh, Trinh Ngoc Kien, Nguyen Duc Anh, Nguyen Hai Hiep, Dinh Tran Vaneckova, Manuela Uher, Tomas Van Schependom, Jeroen Denissen, Stijn Long, Tran Quoc Trung, Nguyen Linh Nagels, Guy |
| author_facet | Huynh, Trinh Ngoc Kien, Nguyen Duc Anh, Nguyen Hai Hiep, Dinh Tran Vaneckova, Manuela Uher, Tomas Van Schependom, Jeroen Denissen, Stijn Long, Tran Quoc Trung, Nguyen Linh Nagels, Guy |
| contents | We present InfoVAE-Med3D, a latent-representation learning approach for 3D brain MRI that targets interpretable biomarkers of cognitive decline. Standard statistical models and shallow machine learning often lack power, while most deep learning methods behave as black boxes. Our method extends InfoVAE to explicitly maximize mutual information between images and latent variables, producing compact, structured embeddings that retain clinically meaningful content. We evaluate on two cohorts: a large healthy-control dataset (n=6527) with chronological age, and a clinical multiple sclerosis dataset from Charles University in Prague (n=904) with age and Symbol Digit Modalities Test (SDMT) scores. The learned latents support accurate brain-age and SDMT regression, preserve key medical attributes, and form intuitive clusters that aid interpretation. Across reconstruction and downstream prediction tasks, InfoVAE-Med3D consistently outperforms other VAE variants, indicating stronger information capture in the embedding space. By uniting predictive performance with interpretability, InfoVAE-Med3D offers a practical path toward MRI-based biomarkers and more transparent analysis of cognitive deterioration in neurological disease. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00051 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Latent Representation Learning from 3D Brain MRI for Interpretable Prediction in Multiple Sclerosis Huynh, Trinh Ngoc Kien, Nguyen Duc Anh, Nguyen Hai Hiep, Dinh Tran Vaneckova, Manuela Uher, Tomas Van Schependom, Jeroen Denissen, Stijn Long, Tran Quoc Trung, Nguyen Linh Nagels, Guy Image and Video Processing Computer Vision and Pattern Recognition Quantitative Methods We present InfoVAE-Med3D, a latent-representation learning approach for 3D brain MRI that targets interpretable biomarkers of cognitive decline. Standard statistical models and shallow machine learning often lack power, while most deep learning methods behave as black boxes. Our method extends InfoVAE to explicitly maximize mutual information between images and latent variables, producing compact, structured embeddings that retain clinically meaningful content. We evaluate on two cohorts: a large healthy-control dataset (n=6527) with chronological age, and a clinical multiple sclerosis dataset from Charles University in Prague (n=904) with age and Symbol Digit Modalities Test (SDMT) scores. The learned latents support accurate brain-age and SDMT regression, preserve key medical attributes, and form intuitive clusters that aid interpretation. Across reconstruction and downstream prediction tasks, InfoVAE-Med3D consistently outperforms other VAE variants, indicating stronger information capture in the embedding space. By uniting predictive performance with interpretability, InfoVAE-Med3D offers a practical path toward MRI-based biomarkers and more transparent analysis of cognitive deterioration in neurological disease. |
| title | Latent Representation Learning from 3D Brain MRI for Interpretable Prediction in Multiple Sclerosis |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Quantitative Methods |
| url | https://arxiv.org/abs/2510.00051 |