SalienceNet

Deep Learning style transfert for nuclei enhancement : https://www.scitepress.org/Papers/2023/116235/116235.pdf

First version : https://www.biorxiv.org/content/10.1101/2022.10.27.514030v1.article-info

Prerequisites

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

Getting Started

Installation

• Clone this repo:

git clone https://github.com/ebouilhol/SalienceNet.git
cd SalienceNet

• Install PyTorch and 0.4+ and other dependencies (e.g., torchvision, visdom and dominate).
  • For pip users : pip install -r requirements.txt.
  • For Conda users, you can create a new Conda environment using : conda env create -f env.yaml.

Download pre-trained model

SalienceNet pre-trained model V0 is available on zenodo : https://zenodo.org/record/7266921/files/salienceNet.zip?download=1

Once downloaded, move it to /SalienceNet/checkpoints and unzip it.

Dataset

To create a dataset please use the following architecture :

└── dataset_folder
    ├── testA
    ├── testB
    ├── trainA
    └── trainB
    

A being the source style dataset and B the target style dataset.

Pretrained model

A pretrained model is available, to use it for prediction use the model name salienceNet :

/!\ The pretrained model is trained on grayscale images with 1 channel, do not forget to use "--input_nc 1 --output_nc 1" as shown below.

#!./scripts/test_cyclegan.sh
python test.py --gpu_ids x --dataroot datasets/dataset_example/ --model cycle_gan --input_nc 1 --output_nc 1 --name salienceNet

CycleGAN train/test

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

• To log training progress and test images to W&B dashboard, set the --use_wandb flag with train and test scrip

• To train a new model:

#!./scripts/train_cyclegan.sh
python train.py  --gpu_ids x --dataroot datasets/dataset_example/ --n_epochs xxx  --model cycle_gan --gan_mode LSSSIMGRAD --name modelname --wcrit1 0.2 --wcrit2 0.2 --wcrit3 0.6

To see more intermediate results, check out ./checkpoints/maps_cyclegan/web/index.html.

• Test the model:

#!./scripts/test_cyclegan.sh
python test.py --gpu_ids x --dataroot datasets/dataset_example/ --model cycle_gan --name modelname

• The test results will be saved to a html file here: ./results/maps_cyclegan/latest_test/index.html.

Acknowledgments

Our code is inspired by pytorch-cycleGAN. For more information regarding the possible test and train option please refer to this github.