This project focuses on developing state-of-the-art deep learning models for medical image segmentation. The primary goal is to create accurate and efficient algorithms for segmenting various anatomical structures in MRI and CT scans.
Key aspects of the project:
- Implementation of U-Net and its variants for image segmentation
- Data augmentation techniques to improve model generalization
- Transfer learning approaches for adapting to different imaging modalities
- Evaluation metrics tailored for medical image analysis
The repository contains code, documentation, and example results demonstrating the effectiveness of these segmentation models in real-world medical imaging scenarios.