- performance: Images with the results of the different approaches
- project: The code of the project
- Autoencoder_aproach: Files used to train and test the different autoencoders. Also, to generate images with the autoencoder
- CNN_approach_files: Files used on the fine-tuning of CNNs approach to classify patients
- Classifier_patches_pacients: Folder with the scripts to classify patches in the autoencoder approach and patients in both approaches
- models: Folder with a script with different autoencoders
- setups: Setups used to train the autoencoders
- setups_clf: Setups used to fine-tune the classifiers
- autoncoder_vs_CNN.ipynb: Comparison of parameters and inference time of both models.
- dataset.py: different pytorch datasets used
- metrics.py: Metrics used as reconstruction error in the autoencoder approach
- partition_dataset.ipynb: Notebook to split the patients in train and test
- preprocessing.ipynb: Notebook to do the image preprocessing
- utils.py: Different helper functions used
- visualize_annotated_classes.py: Visualization of the autoencoder generated images per class
- Results on patches
- Results on patients
