ArticleSoftware impacts2025
MRI-based Alzheimer's disease classification using Vision Transformer and time-series transformer: A step-by-step guide.
Article in Software impacts, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- PCA-Enhanced Deep Features for Alzheimer's Disease Stage Classification with EFMM.Diagnostics (Basel, Switzerland) · 2026Article
- A Dual-Branch Frequency-Aware Attention Framework for Rare Neurological Disease Classification from Brain MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Proto-memformer: deformable memory transformer for Parkinson's MRI classification.Scientific reports · 2026Article
- Alzheimer's disease detection from structural brain MRI using FFA U-Net segmentation and transfer learning.Frontiers in neuroscience · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This study introduces a reproducible pipeline for classifying Alzheimer's Disease from structural brain MRI utilizing a joint transformer architecture that integrates Vision Transformer and Time-Series Transformer models. The proposed framework uses pre-trained ViT for feature extraction from 2D slices of MRI volumes, followed by sequential modeling with a transformer-based classifier to capture inter-slice dependencies. The method is evaluated on the ADNI dataset, involving both binary (AD vs. NC) and multiclass (AD, MCI, NC) classification tasks across axial, sagittal, and coronal planes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.