Deep-Learning-for-Medical-Applications is a repository that compiles deep learning methods, code implementations, and examples applied to medical imaging and healthcare data. The project addresses domain-specific challenges like segmentation, classification, detection, and multimodal data (e.g. MRI, CT, X-ray) using state-of-the-art architectures (e.g. U-Net, ResNet, GAN variants) tailored to medical constraints (small datasets, annotation costs, class imbalance). It includes Jupyter notebooks, model architectures, data preprocessing pipelines, and evaluation scripts specific to medical imaging tasks. The repository may also contain domain-specific modules: loss functions like Dice, focal loss, metrics such as sensitivity/recall/IoU, and visualization utilities for overlaying segmentation masks.
Features
- Model architectures (e.g. U-Net, ResNet, GAN variants) specialized for medical imaging
- Preprocessing pipelines and augmentation techniques for medical data
- Loss functions and metrics suited to segmentation, class imbalance, e.g. Dice, focal loss
- Evaluation and visualization utilities for overlaying predictions on medical images
- Jupyter notebooks showing end-to-end workflows in medical AI tasks
- Emphasis on reproducibility, careful validation, and domain-aware design
Project Samples
