This repository shows an example of how to use MATLAB to produce post-hoc explanations (using Grad-CAM and image LIME) for a medical image classification task.

Both methods (gradCAM and imageLIME) are available as part of the MATLAB Deep Learning toolbox and require only a single line of code to be applied to results of predictions made by a deep neural network (plus a few lines of code to display the results as a colormap overlaid on the actual images).

Example of gradCAM results.

Example of imageLIME results.

Given a chest x-ray (CXR), our solution should classify it into Posteroanterior (PA) or Lateral (L) view.

A small subset of the PadChest dataset¹.

• MATLAB 2020a or later
• Deep Learning Toolbox
• Deep Learning Toolbox™ Model for SqueezeNet Network support package
• Parallel Computing Toolbox (only required for training using a GPU)

1. Download or clone the repository.
2. Open MATLAB.
3. Edit the contents of the dataFolder variable in the xai_medical.mlx file to reflect the path to your selected dataset.
4. Run the xai_medical.mlx script and inspect results.

• You are encouraged to expand and adapt the example to your needs.
• The choice of pretrained network and hyperparameters (learning rate, mini-batch size, number of epochs, etc.) is merely illustrative.
• You are encouraged to (use Experiment Manager app to) tweak those choices and find a better solution.

[1] This example uses a small subset of images to make it easier to get started without having to worry about large downloads and long training times.