This code implements IAIA-BL from the manuscript "A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography" published in Nature Machine Intelligence, Dec 2021, by Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y. Lo, and Cynthia Rudin.

This code package was developed by the authors at Duke University and University of Maine, and licensed as described in LICENSE (for more information regarding the use and the distribution of this code package).

Any operating system on which you can run GPU-accelerated PyTorch. Python 3.6.9. For packages see requirements.txt.

2 NVIDIA Tesla P-100 GPUs or 2 NVIDIA Tesla V-100 GPUs

1. Git clone the repository to /usr/xtmp/IAIABL/.
2. Set up your environment using Python 3.6.9 and requirements.txt. (Optional) Set up your environment using requirements.txt so that "source /home/virtual_envs/ml/bin/activate" activates your environment. You can set up the environment differently if you choose, but all .sh scripts included will attempt to activate the environment at /home/virtual_envs/ml/bin/activate. Typical install time: Less than 10 minutes.

1. In train.sh, the appropriate file locations should be set for train_dir, test_dir, push_dir and finer_dir:

   1. train_dir is the directory containing the augmented training set
   2. test_dir is the directory containing the test set
   3. push_dir is the directory containing the original (unaugmented) training set, onto which prototypes can be projected
   4. finer_dir is the directory containing the augmented set of training examples with fine-scale annotations

2. Run train.sh

No data is provided with this code repository. The following scripts are included to demonstrate how figures and results were created for the paper. The following scripts require data to be provided. Type "source scriptname.sh" into the command line to run.

1. see_explanations.sh

Expected output from see_explanations.sh are figures from the manuscript that begin with "An automatically generated explanation of mass margin classification." The paths to the output images will appear in the relative file location "./visualizations_of_expl/".

2. see_prototype_grid.sh

Expected output from see_prototype_grid.sh will be a grid of prototypes for a given model. The file location where the output image can be found will be printed onto the command line.

3. run_gradCAM.sh

Expected output from run_gradCAM.sh will show the activation precision of the sample data. It will also print a visualization in /usr/xtmp/IAIABL/gradCAM_imgs/view.png. The columns from left to right are "Original Image," "GradCAM heatmap," "GradCAM++ heatmap," "GradCAM heatmap overlayed on the original image," and "GradCAM++ heatmap overlayed on the original image." The rows are "Last layer, using a network trained on natural images," "6th layer, using a network trained on natural images," "Blank," and "Last layer, using a network trained to identify the mass margin."

4. The mal_for_reviewers.ipynb Jupyter notebook is also included.

Expected output from mal_for_reviewers.ipynb is in the cells of the notebook.

Expected run time for these four demo files: 10 minutes.

The following scripts require the more of the (private) dataset in order to run correctly, but are included to aid in reproducibility:

1. dataaugment.sh - for offline data augmentation
2. plot_graph.sh - plots a variety of graphs
3. run_global_analysis.sh - provides a global analysis of the model
4. train_vanilla_malignancy.sh - for training the baseline models

Scripts are set up to expect data as numpy arrays in /usr/xtmp/IAIABL/Lo1136i/test/Circumscribed/ where Circumscribed is the mass margin label. The first channel of the numpy array should be image data and the second (optional) channel should be the fine annotation label.