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Explainable AI for Medical Images

version 1.0 (240 MB) by Oge Marques
Example of how to use MATLAB to produce post-hoc explanations (using Grad-CAM and image LIME) for a medical image classification task.

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Updated 28 Jul 2021

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Explainable AI for Medical Images

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.

Experiment objective

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

Dataset

A small subset of the PadChest dataset1.

Requirements

Suggested steps

  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.

Additional remarks

  • 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.

Notes

[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.

Cite As

Oge Marques (2021). Explainable AI for Medical Images (https://github.com/ogemarques/xai-matlab/releases/tag/1.0), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2021a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.