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arXiv · 2511.11311

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

Petros Koutsouvelis·Matej Gazda·Leroy Volmer·Sina Amirrajab·Kamil Barbierik·Branislav Setlak·Jakub Gazda·Peter Drotar
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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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Petros Koutsouvelis, Matej Gazda, Leroy Volmer, Sina Amirrajab, Kamil Barbierik, Branislav Setlak, Jakub Gazda, Peter Drotar. 2025-11-14. Large-scale modality-invariant foundation models for brain MRI analysis: Application to lesion segmentation. https://arxiv.org/abs/2511.11311

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