Towards XAI in Thyroid Tumor Diagnosis

Our paper is accepted and presented as a long presentation at Health Intelligence Workshop (W3PHIAI-23) at AAAI-23.

Source code for XAI Thyroid - an XAI object detection problem

1. 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.

2. Instruct the parameters to be run with each algorithm

python main.py --help

3. 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

Applicability

• 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.

Results

Citation

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}
}