MRI-based Alzheimer's disease classification using Vision Transformer and time-series transformer: A step-by-step guide
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View abstract on PubMed
Summary
This summary is machine-generated.This study presents a novel pipeline for Alzheimer's Disease (AD) classification using brain MRI scans. The method employs a joint transformer architecture for accurate detection of AD, Mild Cognitive Impairment (MCI), and Normal Control (NC).
Area Of Science
- Neuroimaging
- Artificial Intelligence
- Medical Diagnostics
Background
- Alzheimer's Disease (AD) poses a significant global health challenge.
- Accurate and early diagnosis of AD is crucial for effective management.
- Structural brain Magnetic Resonance Imaging (MRI) offers valuable insights into neurodegeneration.
Purpose Of The Study
- To develop and validate a reproducible pipeline for classifying Alzheimer's Disease using structural brain MRI.
- To leverage a joint transformer architecture integrating Vision Transformer (ViT) and Time-Series Transformer (TST) models.
- To assess the framework's performance in binary (AD vs. Normal Control) and multiclass (AD, Mild Cognitive Impairment, Normal Control) classification tasks.
Main Methods
- A novel pipeline utilizing a joint transformer architecture combining ViT and TST models was developed.
- Pre-trained ViT was employed for feature extraction from 2D MRI slices.
- Sequential modeling with a transformer-based classifier captured inter-slice dependencies for classification.
Main Results
- The proposed framework demonstrated effective classification of Alzheimer's Disease.
- Performance was evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.
- Successful classification was achieved across axial, sagittal, and coronal MRI planes.
Conclusions
- The developed reproducible pipeline offers a promising approach for Alzheimer's Disease classification from structural brain MRI.
- The joint transformer architecture effectively captures spatial and sequential information within MRI volumes.
- This method holds potential for improving early diagnosis and monitoring of Alzheimer's Disease.
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