Public articles linked to the same research event.
arXiv The study introduces ICHOR, a self-supervised pretraining framework for ASL cerebral blood flow (CBF) maps based on 3D masked autoencoders (a ViT-Base encoder with a light decoder), pretrained on 11,405 ASL CBF scans from 14 studies spanning multiple sites and protocols, and evaluated on three diagnostic classification tasks plus one ASL CBF map quality prediction regression task, where it outperformed structural-MRI pretrained baselines BrainIAC, BrainSegFounder, and MedicalNet overall across all four tasks.
The study introduces ICHOR, a self-supervised pretraining framework for ASL cerebral blood flow (CBF) maps based on 3D masked autoencoders (a ViT-Base encoder with a light decoder), pretrained on 11,405 ASL CBF scans from 14 studies spanning multiple sites and protocols, and evaluated on three diagnostic classification tasks plus one ASL CBF map quality prediction regression task, where it outperformed structural-MRI pretrained baselines BrainIAC, BrainSegFounder, and MedicalNet overall across all four tasks.
The study introduces ICHOR, a self-supervised pretraining framework for ASL cerebral blood flow (CBF) maps based on 3D masked autoencoders (a ViT-Base encoder with a light decoder), pretrained on 11,405 ASL CBF scans from 14 studies spanning multiple sites and protocols, and evaluated on three diagnostic classification tasks plus one ASL CBF map quality prediction regression task, where it outperformed structural-MRI pretrained baselines BrainIAC, BrainSegFounder, and MedicalNet overall across all four tasks.
The study introduces ICHOR, a self-supervised pretraining framework for ASL cerebral blood flow (CBF) maps based on 3D masked autoencoders (a ViT-Base encoder with a light decoder), pretrained on 11,405 ASL CBF scans from 14 studies spanning multiple sites and protocols, and evaluated on three diagnostic classification tasks plus one ASL CBF map quality prediction regression task, where it outperformed structural-MRI pretrained baselines BrainIAC, BrainSegFounder, and MedicalNet overall across all four tasks.