Latent Representation Learning from 3D Brain MRI for Interpretable Prediction in Multiple Sclerosis

• URL: http://arxiv.org/abs/2510.00051v1
• Date: Sun, 28 Sep 2025 04:22:56 GMT
• Title: Latent Representation Learning from 3D Brain MRI for Interpretable Prediction in Multiple Sclerosis
• Authors: Trinh Ngoc Huynh, Nguyen Duc Kien, Nguyen Hai Anh, Dinh Tran Hiep, Manuela Vaneckova, Tomas Uher, Jeroen Van Schependom, Stijn Denissen, Tran Quoc Long, Nguyen Linh Trung, Guy Nagels,
• Abstract summary: InfoVAE-Med3D is a latent-representation learning approach for 3D brain MRI that targets interpretable biomarkers of cognitive decline.<n>Our method extends InfoVAE to explicitly maximize mutual information between images and latent variables.
• Score: 1.510486566802232
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Adapting HFMCA to Graph Data: Self-Supervised Learning for Generalizable fMRI Representations [57.054499278843856]
  Functional magnetic resonance imaging (fMRI) analysis faces significant challenges due to limited dataset sizes and domain variability between studies.<n>Traditional self-supervised learning methods inspired by computer vision often rely on positive and negative sample pairs.<n>We propose adapting a recently developed Hierarchical Functional Maximal Correlation Algorithm (HFMCA) to graph-structured fMRI data.
  arXiv  Detail & Related papers  (2025-10-05T12:35:01Z)
• Deeply Supervised Multi-Task Autoencoder for Biological Brain Age estimation using three dimensional T$_1$-weighted magnetic resonance imaging [0.0]
  We propose a Deeply Supervised Multitask Autoencoder (DSMT-AE) framework for brain age estimation.<n>DSMT-AE employs deep supervision, which involves applying supervisory signals at intermediate layers during training.<n>We extensively evaluate DSMT-AE on the Open Brain Health Benchmark dataset.
  arXiv  Detail & Related papers  (2025-08-03T03:24:02Z)
• Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusion [2.7853513988338108]
  Progression Brain Latenttemporal model (BrLP) is designed to predict individual disease-level progression in 3D brain MRIs.<n>We train and evaluate BrLP on 11, T1- Resonance (T1w) brain MRIs from 2,805 subjects and validate its generalizability on an external test set comprising 2,257 MRIs from 962 subjects.
  arXiv  Detail & Related papers  (2025-02-12T16:47:41Z)
• Enhancing Brain Age Estimation with a Multimodal 3D CNN Approach Combining Structural MRI and AI-Synthesized Cerebral Blood Volume Data [14.815462507141163]
  Brain Age Gap Estimation (BrainAGE) offers a neuroimaging biomarker for understanding brain age.<n>Current approaches primarily use T1-weighted magnetic resonance imaging (T1w MRI) data, capturing only structural brain information.<n>We developed a deep learning model using a VGG-based architecture for both modalities and combined their predictions using linear regression.<n>Our model achieved a mean absolute error (MAE) of 3.95 years and an $R2$ of 0.943 on the test set, outperforming existing models trained on similar data.
  arXiv  Detail & Related papers  (2024-12-01T21:54:08Z)
• Knowledge-Guided Prompt Learning for Lifespan Brain MR Image Segmentation [53.70131202548981]
  We present a two-step segmentation framework employing Knowledge-Guided Prompt Learning (KGPL) for brain MRI. Specifically, we first pre-train segmentation models on large-scale datasets with sub-optimal labels. The introduction of knowledge-wise prompts captures semantic relationships between anatomical variability and biological processes.
  arXiv  Detail & Related papers  (2024-07-31T04:32:43Z)
• MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning [48.97640824497327]
  We propose a novel framework leveraging domain-specific medical knowledge as guiding signals to integrate language information into the visual domain through image-text contrastive learning. Our model includes global contrastive learning with our designed divergence encoder, local token-knowledge-patch alignment contrastive learning, and knowledge-guided category-level contrastive learning with expert knowledge. Notably, MLIP surpasses state-of-the-art methods even with limited annotated data, highlighting the potential of multimodal pre-training in advancing medical representation learning.
  arXiv  Detail & Related papers  (2024-02-03T05:48:50Z)
• Triamese-ViT: A 3D-Aware Method for Robust Brain Age Estimation from MRIs [0.7770029179741429]
  This paper introduces Triamese-ViT, an innovative adaptation of the ViT model for brain age estimation. Tested on a dataset of 1351 MRI scans, Triamese-ViT achieves a Mean Absolute Error (MAE) of 3.84, a 0.9 Spearman correlation coefficient with chronological age, and a -0.29 Spearman coefficient correlation between the brain age gap and chronological age.
  arXiv  Detail & Related papers  (2024-01-13T03:29:56Z)
• UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training [66.16134293168535]
  We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
  arXiv  Detail & Related papers  (2023-09-13T09:22:49Z)
• Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction [53.122045119395594]
  Unsupervised anomaly detection (UAD) in brain MRI with deep learning has shown promising results. We propose deep learning for UAD in 3D brain MRI considering additional age information. Based on our analysis, we propose a novel deep learning approach for UAD with multi-task age prediction.
  arXiv  Detail & Related papers  (2022-01-31T09:39:52Z)
• 3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI [55.97060983868787]
  We investigate whether using increased spatial context by using MRI volumes combined with spatial erasing leads to improved unsupervised anomaly segmentation performance. We compare 2D variational autoencoder (VAE) to their 3D counterpart, propose 3D input erasing, and systemically study the impact of the data set size on the performance. Our best performing 3D VAE with input erasing leads to an average DICE score of 31.40% compared to 25.76% for the 2D VAE.
  arXiv  Detail & Related papers  (2021-09-14T09:17:27Z)
• Leveraging 3D Information in Unsupervised Brain MRI Segmentation [1.6148039130053087]
  Unsupervised Anomaly Detection (UAD) methods are proposed, detecting anomalies as outliers of a healthy model learned using a Variational Autoencoder (VAE) Here, we propose to perform UAD in a 3D fashion and compare 2D and 3D VAEs. As a side contribution, we present a new loss function guarantying a robust training. Learning is performed using a multicentric dataset of healthy brain MRIs, and segmentation performances are estimated on White-Matter Hyperintensities and tumors lesions.
  arXiv  Detail & Related papers  (2021-01-26T10:04:57Z)
• Interpretation of 3D CNNs for Brain MRI Data Classification [56.895060189929055]
  We extend the previous findings in gender differences from diffusion-tensor imaging on T1 brain MRI scans. We provide the voxel-wise 3D CNN interpretation comparing the results of three interpretation methods.
  arXiv  Detail & Related papers  (2020-06-20T17:56:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.