This is the code implementation for: "ProGleason-GAN: Conditional Progressive Growing: GAN for prostatic cancer Gleason Grade patch synthesis"
To initiate the training, the following parameters must be set in the config.py file.
| Arguments | Description |
|---|---|
| --START_TRAIN_AT_IMG_SIZE | Start resolution |
| --CHECKPOINT_GEN | Path for generator checkpoint |
| --CHECKPOINT_CRITIC | Path for discriminator checkpoint |
| --RESULTS_DIR | Output directory for the recontructed slides |
| --PATH_CSV_SICAP | Path with SICAPv2 partition annotations |
| --PATH_IMAGES_SICAP | Path containing SICAPv2 patches |
| --DEVICE | DEVICE INFO (cpu or cuda) |
| --SAVE_MODEL | Flag to allow the training to save the model in the RESULTS_DIR |
| --LOAD_MODEL | Flag to allow the training to load previous checkpoints |
| --LEARNING_RATE_GENERATOR | Learning rate for the generator model |
| --LEARNING_RATE_DISCRIMINATOR | Learning rate for the discriminator model |
| --BATCH_SIZES | List of batch sizes for each resolution |
| --CHANNELS_IMG | The number of channels in the input images |
| --Z_DIM | Size of the input noise vector |
| --IN_CHANNELS | The number of channels in the generator's input noise vector |
| --LAMBDA_GP | The weight factor for the gradient penalty term used in the Wasserstein GAN (WGAN) loss |
| --PROGRESSIVE_EPOCHS | List of training epochs for each resolution |
| --N_CLASSES | Number of classes in the dataset |
| --NUM_WORKERS | The number of parallel workers for data loading during training |
After that, you only need to call
$ python train.py