VolTA-3D: Self-Supervised Learning for Brain MRI using 3D Volumetric Token Alignment

• URL: http://arxiv.org/abs/2605.16775v1
• Date: Sat, 16 May 2026 03:09:25 GMT
• Title: VolTA-3D: Self-Supervised Learning for Brain MRI using 3D Volumetric Token Alignment
• Abstract summary: We present Volta-3D, a self-supervised 3D Vision Transformer framework designed to learn transferable representations.<n>We evaluate Volta-3D on multiple out-of-distribution downstream tasks, including hippocampal segmentation and classification of sex and Alzheimer's disease versus healthy controls.
• Score: 7.836810501854232
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervised learning (SSL) has advanced medical image analysis be enabling learning form large unlabelled data. However, in brain magnetic resonance imaging (MRI), most 3D models remain specialized for either segmentation of classification, limiting their ability to generalize across datasets, imaging protocols,, and downstream tasks. This lack of transferability constrains the clinical utility of 3D MRI models, despite the availability of unlabeled volumetric data. We present Volta-3D, a self-supervised 3D Vision Transformer framework designed to learn transferable volumetric representations. Volta-3D jointly aligns global class-style tokens and local patch tokens within a student-teacher paradigm and enforces fine-grained structural reconstruction. This combined global-local alignment addresses the limited semantic diversity and subtle anatomical characteristics of brain MRI, which challenges existing SSL approaches. We evaluate Volta-3D on multiple out-of-distribution downstream tasks, including hippocampal segmentation and classification of sex and Alzheimer's disease versus healthy controls. Across all tasks, representations learned by Volta-3D outperform randomly initialized baselines, demonstrating improved transferability and robustness under domain shift. Hence jointly enforcing global semantic consistency and local structural learning during pretraining enables broader concept learning from unlabeled brain MRI data. Overall VolTA-3D supports effective multi-task downstream performance with task-specific pertaining, a step towards generalizable and clinically viable 3D models.

Related papers

• MAESIL: Masked Autoencoder for Enhanced Self-supervised Medical Image Learning [9.46850781308982]
  Self-Supervised Learning on unlabeled medical data has emerged as a powerful solution, but prominent frameworks often fail to exploit the inherent 3D nature of CT scans.<n>We propose the autoencoder for enhanced self-supervised medical image learning(MAESIL), a novel self-supervised learning framework designed to capture 3D structural information efficiently.<n>Our framework partitions the volume into superpatches and employs a 3D masked autoencoder strategy with a dual-masking strategy to learn comprehensive spatial representations.
  arXiv  Detail & Related papers  (2026-04-01T05:56:39Z)
• MedPruner: Training-Free Hierarchical Token Pruning for Efficient 3D Medical Image Understanding in Vision-Language Models [59.180043227905294]
  We propose MedPruner, a training-free and model-agnostic hierarchical token pruning framework for efficient 3D medical image understanding.<n>We show that MedPruner enables models such as MedGemma to maintain or even exceed their original performance while retaining fewer than 5% of visual tokens.
  arXiv  Detail & Related papers  (2026-03-12T07:37:00Z)
• Exploiting Completeness Perception with Diffusion Transformer for Unified 3D MRI Synthesis [9.857855424798732]
  We propose CoPeDiT, a latent diffusion model equipped with completeness perception for unified synthesis of 3D MRIs.<n>CoPeDiT significantly outperforms state-of-the-art methods, achieving superior robustness, generalizability, and flexibility.
  arXiv  Detail & Related papers  (2026-02-20T18:05:39Z)
• Towards Generalisable Foundation Models for 3D Brain MRI [5.527537739064968]
  We introduce BrainFound, a self-supervised foundation model for brain MRI built by extending DINO-v2.<n>BrainFound adapts DINO-v2 to model full 3D brain anatomy by incorporating information from sequential MRI slices.<n>It supports both single- and multimodal inputs, enabling a broad range of downstream tasks, including disease detection and image segmentation.
  arXiv  Detail & Related papers  (2025-10-27T15:19:46Z)
• M3Ret: Unleashing Zero-shot Multimodal Medical Image Retrieval via Self-Supervision [24.846428105192405]
  We train M3Ret, a unified visual encoder, without any modality-specific customization.<n>It successfully learns transferable representations using both generative (MAE) and contrastive (SimDINO) self-supervised learning (SSL) paradigms.<n>Our approach sets a new state-of-the-art in zero-shot image-to-image retrieval across all individual modalities, surpassing strong baselines such as DINOv3 and the text-supervised BMC-CLIP.
  arXiv  Detail & Related papers  (2025-09-01T10:59:39Z)
• Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning [0.15749416770494706]
  Self-supervised deep learning has accelerated 2D natural image analysis but remains difficult to translate into 3D MRI.<n>We present a emph-sequence-invariant self-supervised framework leveraging quantitative MRI (qMRI)<n> Experiments on healthy brain segmentation (IXI), stroke lesion segmentation (ARC), and MRI denoising show significant gains over baseline SSL approaches.
  arXiv  Detail & Related papers  (2025-01-21T11:27:54Z)
• MG-3D: Multi-Grained Knowledge-Enhanced 3D Medical Vision-Language Pre-training [7.968487067774351]
  3D medical image analysis is pivotal in numerous clinical applications.<n>Large-scale vision-language pre-training remains underexplored in 3D medical image analysis.<n>We propose MG-3D, pre-trained on large-scale data (47.1K)
  arXiv  Detail & Related papers  (2024-12-08T09:45:59Z)
• Autoregressive Sequence Modeling for 3D Medical Image Representation [48.706230961589924]
  We introduce a pioneering method for learning 3D medical image representations through an autoregressive sequence pre-training framework.<n>Our approach various 3D medical images based on spatial, contrast, and semantic correlations, treating them as interconnected visual tokens within a token sequence.
  arXiv  Detail & Related papers  (2024-09-13T10:19:10Z)
• Brain3D: Generating 3D Objects from fMRI [78.46936519561298]
  We design a novel 3D object representation learning method, Brain3D, that takes as input the fMRI data of a subject.<n>We show that our model captures the distinct functionalities of each region of human vision system.<n>Preliminary evaluations indicate that Brain3D can successfully identify the disordered brain regions in simulated scenarios.
  arXiv  Detail & Related papers  (2024-05-24T06:06:11Z)
• Generative Enhancement for 3D Medical Images [74.17066529847546]
  We propose GEM-3D, a novel generative approach to the synthesis of 3D medical images. Our method begins with a 2D slice, noted as the informed slice to serve the patient prior, and propagates the generation process using a 3D segmentation mask. By decomposing the 3D medical images into masks and patient prior information, GEM-3D offers a flexible yet effective solution for generating versatile 3D images.
  arXiv  Detail & Related papers  (2024-03-19T15:57:04Z)
• A self-supervised framework for learning whole slide representations [52.774822784847565]
  We present Slide Pre-trained Transformers (SPT) for gigapixel-scale self-supervision of whole slide images. We benchmark SPT visual representations on five diagnostic tasks across three biomedical microscopy datasets.
  arXiv  Detail & Related papers  (2024-02-09T05:05:28Z)
• Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training [51.16994853817024]
  This work focuses on designing an effective pre-training framework for 3D radiology images. We introduce Disruptive Autoencoders, a pre-training framework that attempts to reconstruct the original image from disruptions created by a combination of local masking and low-level perturbations. The proposed pre-training framework is tested across multiple downstream tasks and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-07-31T17:59:42Z)
• Attentive Symmetric Autoencoder for Brain MRI Segmentation [56.02577247523737]
  We propose a novel Attentive Symmetric Auto-encoder based on Vision Transformer (ViT) for 3D brain MRI segmentation tasks. In the pre-training stage, the proposed auto-encoder pays more attention to reconstruct the informative patches according to the gradient metrics. Experimental results show that our proposed attentive symmetric auto-encoder outperforms the state-of-the-art self-supervised learning methods and medical image segmentation models.
  arXiv  Detail & Related papers  (2022-09-19T09:43:19Z)

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.