MICViT outperforms CNN and transformer baselines on brain age prediction from multimodal 3D MRI by combining modality-specific and cross-modal local/global attention across three heterogeneous datasets.
Towards generalisable foundation models for brain mri
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Pan-FM learns balanced representations across seven organs by adaptively masking dominant organs during pre-training, yielding stronger disease prediction and missing-organ robustness than single-organ or naive multimodal baselines on UK Biobank.
BrainDINO, trained via self-distillation on millions of unlabeled axial brain MRI slices, yields a unified representation that equals or exceeds baselines across diverse neuroimaging tasks when used with a frozen encoder and lightweight heads.
citing papers explorer
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Modeling Local, Global, and Cross-Modal Context in Multimodal 3D MRI
MICViT outperforms CNN and transformer baselines on brain age prediction from multimodal 3D MRI by combining modality-specific and cross-modal local/global attention across three heterogeneous datasets.
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Pan-FM: A Pan-Organ Foundation Model with Saliency-Guided Masking for Missing Robustness
Pan-FM learns balanced representations across seven organs by adaptively masking dominant organs during pre-training, yielding stronger disease prediction and missing-organ robustness than single-organ or naive multimodal baselines on UK Biobank.
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BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning
BrainDINO, trained via self-distillation on millions of unlabeled axial brain MRI slices, yields a unified representation that equals or exceeds baselines across diverse neuroimaging tasks when used with a frozen encoder and lightweight heads.