Artificial Intelligence-based diagnosis of asbestosis: analysis of a database with applicants for asbestosis state-aid 💻 🔎

Managed by Kevin Groot Lipman (k.groot.lipman@nki.nl, k.b.w.grootlipman@gmail.com)

Noninvasive diagnosis of asbestosis for financial compensation suffers from interobserver variability. We developed/integrated an AI-system of multiple components (lung segmentation, anomaly heatmap, classifier) to reproduce the verdict of a panel of medical experts.

Our paper is published at European Radiology at https://link.springer.com/article/10.1007/s00330-022-09304-2

1. Setting up the environment 🌳

Create and activate a conda environment with Python

conda create -n asbestosis python=3.7.9
conda activate asbestosis

Install the requirements

conda install tensorflow-gpu==1.15
conda install keras==2.3.1
conda install pandas
conda install matplotlib==3.2.2
conda install scikit-learn=0.24.1
conda install scipy
pip install pynrrd

Follow instructions at https://github.com/keras-team/keras-contrib to install keras_contrib

2. How to run the code 🚀

2.1 VAE.

• Collect a healthy (CT) dataset.
• Adjust the folders to your own paths for this dataset in data_generator_vae.py and train_vae.py.
• Run python train_vae.py
• After training, run python test_vae.py

2.2 Lung segmentation.

• We adopted the model of Rodney et al. -> https://github.com/lalonderodney/SegCaps

2.3 Resnet training.

• Adjust the folders of your target (disease) dataset to your own paths in data_generator.py and train.py.
• Run python train.py
• After training, run python test.py

❗ You dont have to have anomaly heatmaps (VAE) or lung segmentations to run it. Just set 'n_channels' in train.py to 1 and load from your target dataset folder.

3. Troubleshooting 🔨

If you encounter a h5py error: pip install 'h5py==2.10.0' --force-reinstall

4. Contribution 💪

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated. If you want to add your analysis, or have a suggestion that would make this better, please fork the repo.