CSV-ViT: A Vision Transformer with the Variable-sized Cortical Supervertices for Detection of Alzheimer's Disease Pathologies
📰 ArXiv cs.AI
Learn how CSV-ViT, a Vision Transformer, detects Alzheimer's disease pathologies using variable-sized cortical supervertices on brain cortical surfaces
Action Steps
- Apply CSV-ViT to brain cortical surface data to detect Alzheimer's disease pathologies
- Use variable-sized cortical supervertices to handle non-Euclidean manifolds
- Configure Vision Transformer architecture for spherical topology data
- Test the model on structural MRI data for prescreening
- Compare the performance of CSV-ViT with other surface models
Who Needs to Know This
Neuroscientists, radiologists, and AI researchers can benefit from this technique to improve Alzheimer's disease diagnosis using non-invasive structural MRI-based prescreening
Key Insight
💡 CSV-ViT enables effective learning from cortical surface data for Alzheimer's disease diagnosis
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💡 Detect Alzheimer's disease pathologies with CSV-ViT, a Vision Transformer using variable-sized cortical supervertices on brain cortical surfaces!
Key Takeaways
Learn how CSV-ViT, a Vision Transformer, detects Alzheimer's disease pathologies using variable-sized cortical supervertices on brain cortical surfaces
Full Article
Title: CSV-ViT: A Vision Transformer with the Variable-sized Cortical Supervertices for Detection of Alzheimer's Disease Pathologies
Abstract:
arXiv:2605.26514v1 Announce Type: cross Abstract: Confirming Alzheimer's disease (AD) typically relies on positron emission tomography (PET), which remains costly and invasive, motivating the use of structural MRI-based prescreening. Deep learning on non-Euclidean manifolds, particularly brain cortical surfaces, faces significant challenges due to the data's spherical topology. Recent surface models have enabled learning from cortical surface data; however, imposing face-based uniform patches of
Abstract:
arXiv:2605.26514v1 Announce Type: cross Abstract: Confirming Alzheimer's disease (AD) typically relies on positron emission tomography (PET), which remains costly and invasive, motivating the use of structural MRI-based prescreening. Deep learning on non-Euclidean manifolds, particularly brain cortical surfaces, faces significant challenges due to the data's spherical topology. Recent surface models have enabled learning from cortical surface data; however, imposing face-based uniform patches of
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