StainNet: a fast and robust stain normalization network

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

1、Our approach

You can see the demo.ipynb for details

2、Requirements

Python 3.6 or later with all requirements.txt dependencies installed, including torch>=1.0. To install run:

pip install -r requirements.txt

3、Dataset Download

We 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

4、Train StainGAN

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 

5、Train and Test StainNet

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

test FPS

python test_fps.py

6、Train and eval Classifier

#train Classifier
python train_Classifier.py
#evel Classifier
python eval_Classifier.py