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Section 2: Motivation

motivation directory contains code used to create the example in Section 2: Motivation.

Section 4: Experiments

Main Scripts

  • scripts/lightning_trainer.py trains models on CheXpert dataset.
  • scripts/radimagenet_pretraining.py trains models on RadImageNet dataset.
  • scripts/finetuning_with_masks.py fine-tuning of models on CheXlocalize dataset with masks.
  • scripts/create_train_val_test_split.py creates train/val/test splits for CheXpert dataset.
  • scripts/chexlocalize_finetuned_heatmaps.py generates explanation heatmaps for (mis)aligned models.
  • scripts/chexlocalize_heatmaps.py generates explanation heatmaps for trained models.

Notebooks

  • notebooks/linear_regression_localization_accuracy_analysis.ipynb notebook in which analysis of effects was performed.
  • notebooks/creating_dataset_for_effect_analysis.ipynb notebook in which dataset for analysis was created.
  • notebooks/generate_explanations_plots.ipynb notebook used for creation of explanation plots and example images with masks on CheXlocalize.

Results

results directory contains metadata and code used to create figures and tables in Section 4: Experiments.