Large-scale modality-invariant foundation models for brain MRI analysis: Application to lesion segmentation

• URL: http://arxiv.org/abs/2511.11311v1
• Date: Fri, 14 Nov 2025 13:56:07 GMT
• Title: Large-scale modality-invariant foundation models for brain MRI analysis: Application to lesion segmentation
• Authors: Petros Koutsouvelis, Matej Gazda, Leroy Volmer, Sina Amirrajab, Kamil Barbierik, Branislav Setlak, Jakub Gazda, Peter Drotar,
• Abstract summary: Large-scale foundation model pre-training can learn anatomical priors that improve few-shot performance in neuroimaging tasks.<n>Most SSL frameworks are tailored to natural images, and their adaptation to capture multi-modal MRI information remains underexplored.<n>This work proposes a modality-invariant representation learning setup and evaluates its effectiveness in stroke and epilepsy lesion segmentation.
• Score: 0.4915052615294639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The field of computer vision is undergoing a paradigm shift toward large-scale foundation model pre-training via self-supervised learning (SSL). Leveraging large volumes of unlabeled brain MRI data, such models can learn anatomical priors that improve few-shot performance in diverse neuroimaging tasks. However, most SSL frameworks are tailored to natural images, and their adaptation to capture multi-modal MRI information remains underexplored. This work proposes a modality-invariant representation learning setup and evaluates its effectiveness in stroke and epilepsy lesion segmentation, following large-scale pre-training. Experimental results suggest that despite successful cross-modality alignment, lesion segmentation primarily benefits from preserving fine-grained modality-specific features. Model checkpoints and code are made publicly available.

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