FairNormalization

The code for the paper entitled Equitable Artificial Intelligence for Glaucoma Screening with Fair Identity Normalization. If you have any questions, please email harvardophai@gmail.com and harvardairobotics@gmail.com.

Requirements

To install the prerequisites, run:

pip install - r requirements.txt

Experiments

To run the experiments with the baseline models on 2D RNFLT maps, execute:

./scripts/train_glaucoma_fair_npj.sh

To run the experiments with the baseline models with the proposed FIN module on 3D OCT B-scans, execute:

./scripts/train_glaucoma_fair_proposed_npj.sh

Acknowledgement and Citation

If you find this repository useful for your research, please consider citing our paper:

@article{shi2023equitable,
  title={Equitable Artificial Intelligence for Glaucoma Screening with Fair Identity Normalization},
  author={Shi, Min and Luo, Yan and Tian, Yu and Shen, Lucy Q and Elze, Tobias and Zebardast, Nazlee and Eslami, Mohammad and Kazeminasab, Saber and Boland, Michael V and Friedman, David S and others},
  journal={medRxiv},
  pages={2023--12},
  year={2023},
  publisher={Cold Spring Harbor Laboratory Press}
}

Licence

Apache License 2.0