Architecture Overview
Pioneering Vision Transformers in Neuro-Diagnosis
AlzDetect represents a shift from traditional Convolutional Neural Networks (CNNs) to global-attention based architectures. Our research focuses on leveraging the "patch-based" spatial understanding of Transformers to identify subtle structural biomarkers in MRI scans associated with early-stage Alzheimer's.
Massive Datasets
Trained on over 33,000 augmented MRI images for robust feature extraction.
Global Attention
Global context awareness captures long-range dependencies across brain tissues.
Current Technical Focus
Cross-voxel temporal dependency modeling
Hybrid ViT-CNN feature fusion analysis
Real-time explainable attribution maps
Differential diagnosis via multi-head attention
Methodology
Deep dive into the ViT-B/32 architecture & training logs.
Documentation
Technical guide on deploying and testing the model.
AI Ethics
Clinical ethics, data privacy, and diagnostic integrity.