Kidney Pathology Segmentation
Comparative study of CNNs and Transformer models for CKD gland segmentation using MONAI
Overview
A deep learning project comparing CNN and Transformer-based architectures for segmenting kidney glands in chronic kidney disease (CKD) pathology images.
Key Features
- Model Comparison: Trained and evaluated multiple architectures including UNet, UNetR, and Swin UNetR
- MONAI Framework: Leveraged MONAI (Medical Open Network for AI) for medical image analysis
- Pathology Focus: Specialized in CKD gland segmentation from histopathology images
- Architecture Analysis: Compared performance of CNNs vs. Transformer-based models
Models Evaluated
| Model | Architecture Type | Key Features |
|---|---|---|
| UNet | CNN | Classic encoder-decoder with skip connections |
| UNetR | Transformer | Vision Transformer encoder with CNN decoder |
| Swin UNetR | Transformer | Shifted window attention for efficiency |
Technologies Used
- Framework: MONAI, PyTorch
- Languages: Python
- Tools: Slurm (HPC), Docker
Key Learnings
- Transformer models showed promising results for medical image segmentation
- MONAI provides excellent tools for medical AI development
- Comparison insights useful for selecting appropriate architectures for pathology tasks