Our paper is accepted and presented as a long presentation at Health Intelligence Workshop (W3PHIAI-23) at AAAI-23.
- Install environment
pip install -r requirements.txt
- Download the model at: https://drive.google.com/file/d/1IOyom78mexC6BPq4gPkfFLg-3vPzBnnO/view?usp=sharing
Move the model to model/src/ folder.
- Instruct the parameters to be run with each algorithm
python main.py --help
- Command line example with algorithms Arguments options:
--config-path: path to the configuration file--method: XAI method to run (options: eLRP, GradCAM, GradCAM++, RISE, LIME, DRISE, KDE, DensityMap, AdaSISE)--image-path: path to the image to be processed--stage: stage of the algorithm to be run (options: first_stage, second_stage, default: first_stage)--threshold: threshold of output values to visualize--output-path: path to the output directory
For example, to run the XAI algorithms on images in test_images folder:
- GradCAM
In first stage:
python main.py --config-path xAI_config.json --method GradCAM --image-path data/test_images/ --output-path results/
In second stage:
python main.py --config-path xAI_config.json --method GradCAM --image-path data/test_images/ --stage second_stage --output-path results/
- GradCAM++
In first stage:
python main.py --config-path xAI_config.json --method GradCAM++ --image-path data/test_images/ --output-path results/
In second stage:
python main.py --config-path xAI_config.json --method GradCAM++ --image-path data/test_images/ --stage second_stage --output-path results/
Note: To change input, change the path to new data and path to xml file in xAI_config.json
• Region Proposal Generation (Which proposals are generated by the model during the model’s first stage?): Kernel Density Estimation (KDE), Density map (DM).
• Classification (Which features of an image make the model classify an image containing a nodule(s) at the model’s second stage?): LRP, Grad-CAM, Grad-CAM++, LIME, RISE, Ada-SISE, D-RISE.
• Localization (Which features of an image does the model consider to detect a specific box containing a nodule at the model’s second stage?): D-RISE.
If you find this repository helpful for your research. Please cite our paper as a small support for us too :)
@article{nguyen2023towards,
title={Towards Trust of Explainable AI in Thyroid Nodule Diagnosis},
author={Nguyen, Truong Thanh Hung and Truong, Van Binh and Nguyen, Vo Thanh Khang and Cao, Quoc Hung and Nguyen, Quoc Khanh},
journal={arXiv preprint arXiv:2303.04731},
year={2023}
}
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