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.
Taylor, Nitin Williams, Rhodri Cusack, Tibor Auer, Meredith A
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Deep learning model on large uniform healthy MRI dataset achieves brain age MAE of 4.06 years (hold-out) and 4.21 years (independent set), with frontal lobe prominence and age gaps linked to neuropsychological scores.
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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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Estimating brain age based on a healthy population with deep learning and structural MRI
Deep learning model on large uniform healthy MRI dataset achieves brain age MAE of 4.06 years (hold-out) and 4.21 years (independent set), with frontal lobe prominence and age gaps linked to neuropsychological scores.