This repository contains the PyTorch implementation of our IPMI 2023 paper "On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations".

Wenlong Deng*, Yuan Zhong*, Qi Dou, Xiaoxiao Li

[Paper]

Usage

Setup

pip

See requirement.txt for more environment configurations.

pip install -r requirements.txt

conda

We recommend setting up the environment via conda from exported environment.yaml.

conda env create -f environment.yml
conda activate fcro

Datasets

Please download the original CheXpert dataset here, and supplementary demographic data here.

In our paper, we use an augmented version of low-resolution CheXpert dataset. Please download the metadata of the augmented dataset here, and put it under the ./metadata/ directory.

Pretrained Models

Please download our pretrained models using 5-fold cross validation here, and put them under the ./checkpoint/ directory.

Run a single experiment

python train.py --image_path [image_path] --exp_path [exp_path] --metadata [metadata] \
--lr [lr] --weight_decay [weight_decay] --epoch [epoch] --batch_size [batch_size] \
-a [sensitive_attributes] --dim_rep [dim_rep] -wc [wc] -wr [wr] \
--subspace_thre [subspace_thre] -f [fold] --cond --moving_base --from_sketch

For more information, please execute python train.py -h for help.

Here is an example of how to run a experiment on fold 0 from sketch:

# Train from sketch, i.e., train the sensitive head first, and then the target head.
python train.py --image_path XXX -f 0 --cond --from_sketch

Here is another example of how to train the target model using a pretrained sensitive model:

python train.py --image_path XXX -f 0 --cond

By default, the pretrained sensitive model under the ./checkpoint/ directory will be used. If you want to customize it, please use --pretrained_path option.

To calculate column orthogonal loss using accumulative space construction variant, please use--moving_space option.

Test

After installing our pretrained model and metadata, you can reproduce our 5-fold cross validation results in our paper by running:

# Running test using model of fold 0. Please run full 5-fold to reproduce our results.
python train.py --test --image_path XXX -f 0

You may customize --pretrained_path and --sensitive_attributes commands to use other pretrained models or test on other sensitive attributes combinations.

Citation

If you find this work helpful, feel free to cite our paper as follows:

@article{deng2023fairness,
  title={On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations},
  author={Deng, Wenlong and Zhong, Yuan and Dou, Qi and Li, Xiaoxiao},
  journal={arXiv preprint arXiv:2301.01481},
  year={2023}
}