Advanced Medical Imaging via ViT
Published deep learning research utilizing custom Vision Transformers (ViT) for automated brain tumor classification with 96% accuracy.
Computer Vision & Clinical Diagnostics
This project highlights published deep learning research utilizing a custom-implemented Vision Transformer (ViT) architecture to automate brain tumor classification from clinical MRI scans.
By utilizing self-supervised representation learning and advanced transformer backbones, the system extracts rich spatial and architectural features directly from scan slices, streamlining clinical diagnostic pipelines.
Core Research Metrics:
- Dataset Scale: Trained and benchmarked across a high-volume medical dataset containing over 50,000+ individual MRI slices.
- Model Performance: Achieved a verified model classification accuracy of 96%.
- Self-Supervised Pre-training: Implemented a robust contrastive learning framework (SimCLR) to optimize structural representations prior to supervised fine-tuning.