Codebase for the paper ViT-AE++: Improving Vision Transformer Autoencoder for Self-supervised Medical Image Representations
Run brats_split.py and egd_split.py files in the bootstrap folder to create dataset splits.
Note: The base_dir path needs to be updated in both files. It should point to the local storage structure.
Similarly, the BASE_PATH in the brats_dataset/brats.py and egd_dataset/egd.py needs to be updated accordingly.
All the files use argparse and support command line arguments. However, configurations in the config.ini file can override any configuration given from the command line.
Some configurations in this file are essential such as the ones in the K_FOLD section. These determine the number of epochs, output_folder, logging directory, etc.
Download ckp-399.pth pretrained model weights from here and save it in the model folder. The model weights are necessary for using the perceptual loss.
Please use k_fold_cross_valid_combined_brats.py and k_fold_cross_valid_combined_egd.py to train models and extract ssl features for each split.
There are also scripts to train a 3D resnet or train the model in two stages (first autoencoder and then contrastive training). However, these are experimental codes and not suitable for model training.
The trained ViT model can be used as a feature extractor with an appropriate model weight and dataset split. The relevant code is present in post_training_utils/extract_ssl_features.py file. Please make changes in the EXTRACT_SSL section in the config.ini file. Also, please be mindful of the training data split
that you have chosen since the two datasets are not symmetric. Please refer to the dataset files for reference.
The extracted SSL features are evaluated using classical models such as random forest, SVM, and logistic regression. The relevant script is present in feature_evaluation_script/evaluation_k_fold_[Dataset].py where
Dataset can be egd or brats. Please feel free to play around and train different models for the extracted features.