BrainFIBRE pretrains a five-expert Mixture-of-Experts model on NODDI-derived microstructural maps and outperforms prior deep models on age, sex, cerebrovascular, neurodegenerative, and cognitive prediction.
In: International conference on machine learning
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MFASSL adds mirror-paired views, a lightweight Mirror-Fusion Attention module, and reflection-consistency losses to improve SSL on bilateral data with ~2.7% extra parameters.
A sequential-to-global SSL method based on DINO pretrains iterative foveal-inspired vision transformers to achieve competitive ImageNet-1K performance with constant compute regardless of input resolution.
MoVA introduces modular asymmetric dual projections to handle temporal misalignment and semantic asymmetry in long video-text alignment.
Self-supervised contrastive learning adapts ViT for cardiac MR classification, outperforming supervised training with AUC >0.75 on four common sequences and generalization to BraTS and ADNI.
citing papers explorer
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BrainFIBRE: A Foundation Model via Information Decomposition for Brain Microstructure
BrainFIBRE pretrains a five-expert Mixture-of-Experts model on NODDI-derived microstructural maps and outperforms prior deep models on age, sex, cerebrovascular, neurodegenerative, and cognitive prediction.
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Mirror-Fusion Attention for Reflection-Aware Self-Supervised Representation Learning
MFASSL adds mirror-paired views, a lightweight Mirror-Fusion Attention module, and reflection-consistency losses to improve SSL on bilateral data with ~2.7% extra parameters.
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Self-supervised pretraining for an iterative image size agnostic vision transformer
A sequential-to-global SSL method based on DINO pretrains iterative foveal-inspired vision transformers to achieve competitive ImageNet-1K performance with constant compute regardless of input resolution.
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MoVA: Learning Asymmetric Dual Projections for Modular Long Video-Text Alignment
MoVA introduces modular asymmetric dual projections to handle temporal misalignment and semantic asymmetry in long video-text alignment.
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Self-Supervised Contrastive Learning for Cardiac MR Sequence Classification
Self-supervised contrastive learning adapts ViT for cardiac MR classification, outperforming supervised training with AUC >0.75 on four common sequences and generalization to BraTS and ADNI.