In this work, we introduce FFPE++ to improve the quality of FFPE tissue sections using an unpaired image-to-image translation technique that converts FFPE images with artifacts into high-quality FFPE images without the need for explicit image pairing and annotation.

• Linux or macOS
• Python 3
• CPU or NVIDIA GPU + CUDA CuDNN

• Clone this repo:

git clone https://github.com/DeepMIALab/FFPE
cd FFPEPlus   

• Install PyTorch 1.1 and other dependencies (e.g., torchvision, visdom, dominate, gputil).

• For pip users, please type the command pip install -r requirements.txt.

• For Conda users, you can create a new Conda environment using conda env create -f environment.yml.

• To replicate the results, you may download OV project for Ovary, LUAD and LUSC projects for Lung, and THCA project for Thyroid from TCGA Data Portal and create a subset using these .txt files.
• To extract the patches from WSIs and create PNG files, please follow the instructions given in FFPEPlus/Data_preprocess section.

The data used for training are expected to be organized as follows:

Data_Path                # DIR_TO_TRAIN_DATASET
 ├──  trainA
 |      ├── 1.png     
 |      ├── ...
 |      └── n.png
 ├──  trainB     
 |      ├── 1.png     
 |      ├── ...
 |      └── m.png
 ├──  testA
 |      ├── 1.png     
 |      ├── ...
 |      └── j.png
 └──  testB     
        ├── 1.png     
        ├── ...
        └── k.png

• To view training results and loss plots, run python -m visdom.server and click the URL http://localhost:8097.

• Train the FFPE++ model:

python train.py --dataroot ./datasets/Frozen/${dataroot_train_dir_name} --name ${model_results_dir_name} --CUT_mode CUT --batch_size 1

• Test the FFPE++ model:

python test.py --dataroot ./datasets/Frozen/${dataroot_test_dir_name}  --name ${result_dir_name} --CUT_mode CUT --phase test --epoch ${epoch_number} --num_test ${number_of_test_images}

The test results will be saved to a html file here: ./results/${result_dir_name}/latest_train/index.html

If you find our work useful in your research or if you use parts of this code please consider citing our paper:

Our code is developed based on CUT.