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HE_adversarial_CNN

Implementation of H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression.

Reference

If you find this repository useful in your research, please cite:

[1] Marini, N., Atzori, M., Otálora, S., Marchand-Maillet, S., & Müller, H. H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression.

Paper link: https://openaccess.thecvf.com/content/ICCV2021W/CDPath/papers/Marini_HE-Adversarial_Network_A_Convolutional_Neural_Network_To_Learn_Stain-Invariant_Features_ICCVW_2021_paper.pdf

Requirements

Python==3.6.9, albumentations==0.1.8, numpy==1.17.3, opencv==4.2.0, pandas==0.25.2, pillow==6.1.0, torchvision==0.8.1, pytorch==1.7.0

CSV Input Files:

CSV files are used as input for the scripts. For each partition (train, validation, test), the csv file has path_to_image, class_label as columns. For prostate experiments, the class_label can be: 0: benign 1: Gleason pattern 3 2: Gleason pattern 4 3: Gleason pattern 5 For colon experiments, the class_label can be: 0: cancer 1: dysplasia 2: normal glands

Training

Scripts to train the CNN at path-level, in a fully-supervised fashion. Some parameters must be manually changed, such as the number of classes (output of the network).

  • Training_script.py -n -b -c -e -t -f -i -o. The script is used to train the CNN without any augmentation (no_augment), with colour augmentation (augment).

    • -n: number of the experiment for the training
    • -b: batch size (32)
    • -c: CNN backbone to use (densenet121)
    • -e: number of epochs (10)
    • -t: task of the network (no_augment, augment, normalizer)
    • -f: if True an embedding layer with 128 nodes is inserted before the output layer
    • -i: path of the folder where the input csvs for training (train.csv), validation (valid.csv) and testing (test.csv) are stored
    • -o: path of the folder where to store the CNN’s weights.

  • Training_stain_augmentation.py -n -b -c -e -t -f -i -o. The script is used to train the CNN with stain augmentation method.

    • -n: number of the experiment for the training
    • -b: batch size (32)
    • -c: CNN backbone to use (densenet121)
    • -e: number of epochs (10)
    • -t: task of the network (no_augment, augment)
    • -f: if True an embedding layer with 128 nodes is inserted before the output layer
    • -i: path of the folder where the input csvs for training (train.csv), validation (valid.csv) and testing (test.csv) are stored
    • -o: path of the folder where to store the CNN’s weights.

  • Training_CNN_stain_normalizer.py -n -b -c -e -t -f -i -o. The script is used to train the model using StainCNNs to normalize the images (from https://github.com/alexmomeni/StainGAN).

    • -n: number of the experiment for the training
    • -b: batch size (32)
    • -c: CNN backbone to use (densenet121)
    • -e: number of epochs (10)
    • -t: task of the network (no_augment, augment)
    • -f: if True an embedding layer with 128 nodes is inserted before the output layer
    • -i: path of the folder where the input csvs for training (train.csv), validation (valid.csv) and testing (test.csv) are stored
    • -o: path of the folder where to store the CNN’s weights.

  • Training_script_domain_adversarial_center.py -n -b -c -e -t -f -i -o. The script is used to train the CNN using a domain adversarial CNN. Data generator must be modified, inserting the condition to distinguish the images from different domains (in this paper the path).

    • -n: number of the experiment for the training
    • -b: batch size (32)
    • -c: CNN backbone to use (densenet121)
    • -e: number of epochs (10)
    • -t: task of the network (domain_center)
    • -f: if True an embedding layer with 128 nodes is inserted before the output layer
    • -i: path of the folder where the input csvs for training (train.csv), validation (valid.csv) and testing (test.csv) are stored
    • -o: path of the folder where to store the CNN’s weights.

  • Training_script_domain_adversarial_regressor.py -n -b -c -e -t -f -i -o. The script is used to train H&E-adversarial network.

    • -n: number of the experiment for the training
    • -b: batch size (32)
    • -c: CNN backbone to use (densenet121)
    • -e: number of epochs (10)
    • -t: task of the network (domain_regressor)
    • -f: if True an embedding layer with 128 nodes is inserted before the output layer
    • -i: path of the folder where the input csvs for training (train.csv), validation (valid.csv) and testing (test.csv) are stored
    • -o: path of the folder where to store the CNN’s weights.

Testing

Scripts to test the CNNs.

  • Testing_script.py -n -b -c -t -f -d -m -i. The script is used to test the CNN trained using no_augment, augment, stain_augment

    • -n: number of the experiment for the training
    • -b: batch size
    • -c: CNN backbone to use (densenet121)
    • -t: task of the network (no_augment, augment, stain_augment, normalizer). In case the task is “normalizer” the image used as target must be the same used during the training.
    • -f: if True an embedding layer with 128 nodes is inserted before the output layer. It must be the same used to train the model
    • -d: name of the dataset to use.
    • -m: path where the model is stored.
    • -i: path where the csv input files are stored.

  • Testing_script_CNN_normalized.py. -n -b -c -t -f -d -m -i. The script is used to test the CNN trained using StainCNNs to normalize the images.

    • -n: number of the experiment for the training
    • -b: batch size
    • -c: CNN backbone to use (densenet121)
    • -t: task of the network (StainGAN, StainNet)
    • -f: if True an embedding layer with 128 nodes is inserted before the output layer. It must be the same used to train the model
    • -d: name of the dataset to use.
    • -m: path where the model is stored.
    • -i: path where the csv input files are stored.

  • Testing_script_domain_adversarial_center.py. -n -b -c -t -f -d -m -i. The script is used to test the CNN trained using domain_adversarial network

    • -n: number of the experiment for the training
    • -b: batch size
    • -c: CNN backbone to use (densenet121)
    • -t: task of the network (domain_center)
    • -f: if True an embedding layer with 128 nodes is inserted before the output layer. It must be the same used to train the model
    • -d: name of the dataset to use.
    • -m: path where the model is stored.
    • -i: path where the csv input files are stored.

  • Testing_script_domain_adversarial_regressor.py. -n -b -c -t -f -d -m -i. The script is used to test the CNN trained using no_augment, augment, stain_augment

    • -n: number of the experiment for the training
    • -b: batch size
    • -c: CNN backbone to use (densenet121)
    • -t: task of the network (domain_regressor)
    • -f: if True an embedding layer with 128 nodes is inserted before the output layer. It must be the same used to train the model
    • -d: name of the dataset to use.
    • -m: path where the model is stored.
    • -i: path where the csv input files are stored.

Acknoledgements

This project has received funding from the EuropeanUnion’s Horizon 2020 research and innovation programme under grant agree-ment No. 825292 ExaMode. Infrastructure fromthe SURFsara HPC center was used to train the CNN models in parallel.