Last Updated: 27/04/2025
The official implementation of DGR, a generative AI model for virtual staining in histopathology workflows.
DGR is a novel framework designed for virtual staining of histopathology images with enhanced resistance to misalignment. Our method enables:
- High-fidelity stain transformation between different histopathology modalities
- Robust performance despite common tissue section misalignments
- Significant acceleration of histopathology workflows
- 🚀 High-quality transformations
- 🔄 Misalignment-resistant
- ⏱️ Fast inference
- 📊 Multi-dataset support
- 🧠 Modular architecture
- Clone this repository:
git clone https://github.com/birkhoffkiki/DTR.git
cd DTR
conda create --name DTR python=3.9
conda activate DTR
pip install -r requirements.txt- Aperio-Hamamatsu dataset: https://github.com/khtao/StainNet
- HEMIT dataset: https://github.com/BianChang/HEMIT-DATASET
# For Aperio-Hamamatsu dataset
bash train_aperio.sh
# For HEMIT dataset
bash train_hemit.sh| Model Name | Download Link |
|---|---|
| AF2HE Weight | Download |
| HE2PAS Weight | Download |
| HEMIT Weight | Download |
| Aperio Weight | Download |
Example notebook: play_with_the_pretrained_model.ipynb
if you have any questions, please feel free to contact me:
- JIABO MA, jmabq@connect.ust.hk