ArticleProceedings. IEEE International Symposium on Biomedical Imaging2024
PVTAD: ALZHEIMER'S DISEASE DIAGNOSIS USING PYRAMID VISION TRANSFORMER APPLIED TO WHITE MATTER OF T1-WEIGHTED STRUCTURAL MRI DATA.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Detection of Alzheimer Disease in Neuroimages Using Vision Transformers: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- The ICN-UN Battery: A Machine Learning-Optimized Tool for Expeditious Alzheimer's Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Mind the Gap: Does Brain Age Improve Alzheimer's Disease Prediction?Human brain mapping · 2025Article
- Deep learning-based Alzheimer's disease detection using magnetic resonance imaging and gene expression data.PloS one · 2025Article
- Identifying Alzheimer's disease-associated genes using PhenoGeneRanker.bioRxiv : the preprint server for biology · 2024Article
- A Feature-Fusion Technique-Based Alzheimer's Disease Classification Using Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2024Article
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4 authors.
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Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder, and timely diagnosis is crucial for early interventions. AD is known to have disruptive local and global brain neural connections that may be instrumental in understanding and extracting specific biomarkers. Existing machine-learning approaches are mostly based on convolutional neural network (CNN) and standard vision transformer (ViT) models, which may not sufficiently capture the multidimensional local and global patterns indicative of AD. Therefore, in this paper, we propose a novel approach called
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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.