Advanced Medical Imaging via ViT
Deep learning research utilizing Vision Transformers (ViT) and self-supervised contrastive learning (SimCLR) for brain tumor detection.
Self-Supervised Contrastive Learning with Vision Transformers
This research project aims to enhance brain tumor detection accuracy using state-of-the-art Vision Transformers (ViT) combined with SimCLR (Simple Framework for Contrastive Learning of Visual Representations). By leveraging self-supervised pre-training on unlabeled MRI datasets, the model learns robust structural representations before fine-tuning on annotated clinical scans.
Key Features:
- Vision Transformer Backbone: High-capacity spatial feature extraction.
- Self-Supervised SimCLR: Unsupervised pre-training to optimize contrastive representations.
- Clinical Segmentation: Accurate tumor boundary delineation and classification.