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    MR-JEPA Video Foundation Model for Cardiac MRI

    Tanishq Mathew Abraham posts arXiv paper on self-supervised 3D cardiac MRI model.

    TM
    1 Source, 29d ago, first seen 29d ago

    TLDR

    Tanishq Mathew Abraham announced MR-JEPA, a self-supervised video foundation model for Cardiac MRI. The work extends LeJEPA to 3D spatiotemporal inputs through tubelet tokenization, spatiotemporal masking augmentation, and initialization from a 2D Cardiac MRI foundation model. The linked arXiv paper states that cardiac magnetic resonance imaging produces rich sequential data such as temporal cine videos and spatial LGE/mapping stacks, yet most deep learning approaches process individual 2D slices.

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    1 Source, first seen 29d ago

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    1 Source, first seen 29d ago

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    1 Source

    @iScienceLuvrMR-JEPA: A General Purpose Video Foundation Model for Cardiac MRI "We present MR-JEPA, a self-supervised video foundation model for Cardiac MRI that extends LeJEPA to 3D spatiotemporal inputs through tubelet tokenization, spatiotemporal masking augmentation, and initialization from a 2D Cardiac MRI foundation model." "MR-JEPA outperforms both a natural-video foundation model (V-JEPA2, [4]) and a prior CMR-specific foundation model [22] on all regression tasks while remaining competitive for disease classification, despite using 5x fewer pretraining videos and a smaller architecture." paper link: https://arxiv.org/abs/2608.30975

    1 Source

    @iScienceLuvrMR-JEPA: A General Purpose Video Foundation Model for Cardiac MRI "We present MR-JEPA, a self-supervised video foundation model for Cardiac MRI that extends LeJEPA to 3D spatiotemporal inputs through tubelet tokenization, spatiotemporal masking augmentation, and initialization from a 2D Cardiac MRI foundation model." "MR-JEPA outperforms both a natural-video foundation model (V-JEPA2, [4]) and a prior CMR-specific foundation model [22] on all regression tasks while remaining competitive for disease classification, despite using 5x fewer pretraining videos and a smaller architecture." paper link: https://arxiv.org/abs/2608.30975