{"ID":2841730,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.11311","arxiv_id":"2511.11311","title":"Large-scale modality-invariant foundation models for brain MRI analysis: Application to lesion segmentation","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.","short_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 SS...","url_abs":"https://arxiv.org/abs/2511.11311","url_pdf":"https://arxiv.org/pdf/2511.11311v2","authors":"[\"Petros Koutsouvelis\",\"Matej Gazda\",\"Leroy Volmer\",\"Sina Amirrajab\",\"Kamil Barbierik\",\"Branislav Setlak\",\"Jakub Gazda\",\"Peter Drotar\"]","published":"2025-11-14T13:56:07Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.AI\",\"cs.CV\",\"cs.LG\"]","methods":"[]","has_code":false}