Large-scale modality-invariant foundation models for brain MRI analysis: Application to lesion segmentation
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Koutsouvelis, Petros Gazda, Matej Volmer, Leroy Amirrajab, Sina Barbierik, Kamil Setlak, Branislav Gazda, Jakub Drotar, Peter |
| author_facet | Koutsouvelis, Petros Gazda, Matej Volmer, Leroy Amirrajab, Sina Barbierik, Kamil Setlak, Branislav Gazda, Jakub Drotar, Peter |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11311 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large-scale modality-invariant foundation models for brain MRI analysis: Application to lesion segmentation Koutsouvelis, Petros Gazda, Matej Volmer, Leroy Amirrajab, Sina Barbierik, Kamil Setlak, Branislav Gazda, Jakub Drotar, Peter Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Large-scale modality-invariant foundation models for brain MRI analysis: Application to lesion segmentation |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2511.11311 |