Tutorial

This is the code for our paper titled "Weakly-Supervised Deep Learning Model for Prostate Cancer Diagnosis and Gleason Grading of Histopathology Images".

Dependencies

• Pandas == 1.3.5
• NumPy == 1.21.6
• skimage == 0.18.3
• tiatoolbox == 1.0.1
• opencv == 4.5.5
• Tensorflow == 2.3
• sklearn == 1.0.2
• tqdm == 4.64.0
• scipy == 1.7.3
• Pytorch == 1.12.1

Code Usage

Running the code has 6 steps which are as follows:

• Step 1: Cropping the WSIs to small patches.
  • The following command can be used to run the code for this step:
    • --mode: for this step model should be crop
    • --wsi_dir: the directory of the WSIs, default = 'images/'
    • --labels: the directory of the clinical file containing the scores. It should be a csv file containing a column named image_id for the name of images, a column named primary_gleason_grade, and another column named secondary_gleason_grade for the primary and secondary Gleason grades. default = 'clinical.csv'
    • --patch_size: size of small patches. default = 512
    • Note: this command saves the patches and labels in Preprocessed_data//patches and Preprocessed_data//labels, respectively.
• Step 2: Train the Autoencoder.
  • The following commands can be used to train the autoencoder:
    • --mode: for this step model should be auto
    • --n_bag: number of bags to train the autoencoder. default = 500
    • --lr_a: learning rate for autoencoder. default = 1e-3
    • --batch_a: batch size for autoencoder. default = 4
    • --epoch_a: number of epochs to train autoencoder. default = 50
    • --auto_dir: path to save autoencoder. default = autoencoder//
    • Note: this command reads the patches from Preprocessed_data//patches.
• Step 3: Extract the feature vectors from small patches using the encoder.
  • The following commands can be used to extract the features:
    • --mode: for this step model should be feature
    • --auto_dir: path to the autoencoder. default = autoencoder//
    • Note: this command reads the patches from Preprocessed_data//patches and saves the features in Preprocessed_data//features.
• Step 4: Train the first stage of the model and extract the scores.
  • The following commands should be used for this step:
    • --mode: for this step model should be model1
    • --state1: it can be either traintest to train and save the scores or test to load the model and extract the scores. default = traintest
      • Note: the model will be saved/loaded from Models//model1
    • --score_dir: the path to save the scores. default = Preprocessed_data//scores
    • --epoch: number of epochs. default = 1000
    • --lr: learning rate. default = 5e-5
    • --wd: weight decay. default = 10e-6
    • --gate: whether using gated attention or not. default = True
    • --hid_dim: hidden dimension in the linear layer. default = 512
    • --out_dim: the output dim in the linear layer. default = 256.
    • --n_class: number of class in the data. default = 5.
    • Note: this command reads the features from Preprocessed_data//features and labels from Preprocessed_data//labels.
• Step 5: Create the graphs from the discriminative patches.
  • The following commands are for this step:
    • --mode: for this step model should be graph
    • --scores_dir: path to the scores extracted by the first stage of the model. default = Preprocessed_data//scores//
    • --n_neighbor: number of neighbors in KNN. default = 10
    • --top: choose the top% of high score. default = 40
    • Note: the graphs will be saved in Preprocessed_data//graphs.
• Step 6: Train the graph convolutional neural network.
  • The following commands are used for this step:
    • --mode: for this step model should be model2
    • --epoch: number of epochs. default = 1000
    • --lr: learning rate. default = 5e-5
    • --wd: weight decay. default = 10e-6
    • --GAT: whether using GAT or GCN. default = False
    • --hid_dim: hidden dimension in the linear layer. default = 512
    • --out_dim: the output dims in the linear layer. default = 256.
    • --n_class: number of classes in the data. default = 5.
    • --n_heads: number of attention layers. default = 6.
    • --gat_heads: number of gated attention layers. default = 4.
    • --fold: number of folds in the cross-validation. default = 10.
    • --dense_dim: dim of the last linear layer. default = 16.
    • Note: this command reads the graph from Preprocessed_data//graphs and labels from Preprocessed_data//labels.

Using Example

For step 1:

python main.py --mode crop --wsi_dir images// --labels clinical.csv --patch_size 512

For step 2:

python main.py --mode auto --n_bag 200 --epoch_a 25

For step 3:

python main.py --mode feature --auto_dir Autoencoder//

For step 4:

python main.py --mode model1 --state1 traintest --epoch 2500

For step 5:

python main.py --mode graph --n_neighbor 15 --top 50

For step 6:

python main.py --mode model2 --lr 1e-5 --wd 1e-6 --GAT False --fold 5 --n_heads 4

Citation

@misc{behzadi2022weaklysupervised,
      title={Weakly-Supervised Deep Learning Model for Prostate Cancer Diagnosis and Gleason Grading of Histopathology Images}, 
      author={Mohammad Mahdi Behzadi and Mohammad Madani and Hanzhang Wang and Jun Bai and Ankit Bhardwaj and Anna Tarakanova and Harold Yamase and Ga Hie Nam and  Sheida Nabavi},
      year={2022},
      eprint={2212.12844},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}