The code and dataset for StainNet with url: https://doi.org/10.3389/fmed.2021.746307 please cite our paper if you use the code or dataset
You can see the demo.ipynb for details
Python 3.6 or later with all requirements.txt dependencies installed, including torch>=1.0. To install run:
pip install -r requirements.txtWe make the aligned histopathology dataset and the histopathology classification dataset in our paper publicly available. They can be downloaded from BaiduYun as follow:
BaiduYun url:https://pan.baidu.com/s/1_k7l3wL0vrP26Yc6kkcWEQ Extraction code：wrfi
The related descriptions are below:
| Filename | Descriptions |
|---|---|
| aperio_hamamatsu.zip | The aligned histopathology dataset from the publicly available part of the MITOS-ATYPIA ICPR’14 challenge. |
| camelyon16.zip | The histopathology classification dataset from the publicly available Camelyon16 dataset for train classifier |
| camelyon16_for_train_norm.zip | From the publicly available Camelyon16 dataset for train normalization model |
git clone https://github.com/khtao/StainGAN.git cd StainGAN # train the aligned histopathology dataset python train.py --dataroot aperio_hamamatsu/train --phaseA aperio --phaseB hamamatsu --batchSize 4 --niter 25 --niter_decay 25 --loadSize 256 --fineSize 256 --name aperio_hamamatsu --display_env aperio-hamamatsu --model cycle_gan --no_dropout # train the histopathology classification dataset for norm python train.py --dataroot camelyon16_for_train_norm --phaseA centerUni --phaseB centerRad --batchSize 4 --niter 100 --niter_decay 100 --loadSize 256 --fineSize 256 --name aperio_hamamatsu --display_env aperio-hamamatsu --model cycle_gan --no_dropout python test.py -h --source_dir SOURCE_DIR path to source images for test --gt_dir GT_DIR path to ground truth images for test --method METHOD different methods for test must be one of { StainNet StainGAN reinhard macenko vahadane khan } --test_ssim whether calculate SSIM , default is False --random_target random choose target or using matched ground truth, True is random choose target --input_nc INPUT_NC # of input image channels --output_nc OUTPUT_NC # of output image channels --channels CHANNELS # of channels in StainNet --n_layer N_LAYER # of layers in StainNet --model_path MODEL_PATH models path to load python train_StainNet.py -h --name NAME name of the experiment. --source_root_train SOURCE_ROOT_TRAIN path to source images for training --gt_root_train GT_ROOT_TRAIN path to ground truth images for training --source_root_test SOURCE_ROOT_TEST path to source images for test --gt_root_test GT_ROOT_TEST path to ground truth images for test --input_nc INPUT_NC # of input image channels --output_nc OUTPUT_NC # of output image channels --channels CHANNELS # of channels in StainNet --n_layer N_LAYER # of layers in StainNet --batchSize BATCHSIZE input batch size --nThreads NTHREADS # threads for loading data --checkpoints_dir CHECKPOINTS_DIR models are saved here --fineSize FINESIZE crop to this size --display_freq DISPLAY_FREQ frequency of showing training results on screen --test_freq TEST_FREQ frequency of cal --lr LR initial learning rate for SGD --epoch EPOCH how many epoch to train python test_fps.py#train Classifier python train_Classifier.py #evel Classifier python eval_Classifier.py