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MyThisYourThat: Interpretable Identification of Systematic Bias in Federated Learning for Biomedical Images

☕ This repository contains code for the MyTH model, a novel extension of ProtoPNet (Chen et al.) for interpretable and privacy-preserving identification of data bias in federated learning for images. The materials necessary to implement the baseline original ProtoPNet were adopted from this repository.

MyTH will also be soon available within DISCO web platform for collaborative model training (GitHub repository).

Here are the steps to reproduce our approach.

🗃️ Data preprocessing

We used the CheXpert dataset of chest X-ray images that can be downloaded here. After you download and unzip the archive.zip file, please, create a new folder named CheXpert-v1.0-small and move the unzipped train folder inside. Finally, ensure that the CheXpert-v1.0-small folder and train.csv file are in the current repository.

In this work, we do one-vs-rest classification. For this aim, it is needed to separate images belonging to the class of interest (positive class) from all others (negative class). For cardiomegaly and pleural effusion classification, this can be done by running preprocessing_cardio.py and prerocessing_effusion.py scripts, respectively. The scripts will create folders with three subfolders containing images for training and validation as well as push subfolder with a subset of training images for prototype visualization.

We experimented with two distinct types of data bias. A synthetic bias for the cardiomegaly classification can be added directly within the training scripts described in the next section. However, to introduce a real-world chest drain bias into the pleural effusion class, a separate dataset should be used. To prepare it, run the preprocessing_drains.py script which will output a corresponding drains folder. The labels for the presence of chest drains were adopted from Jiménez-Sánchez A. et al..

💡 Training

  1. Centralized Model (CM). To train a baseline ProtoPNet in a centralized setting, run the run_CheXpert_CM.py script providing the following arguments:
  • -nc number of classes which is 2 by default in our one-vs-rest approach;
  • -e number of training epochs, we used around 20 for pleural effusion and 30 for cardiomegaly;
  • -c class name which is either cardiomegaly or effusion

Example:

python run_CheXpert_CM.py -e 22 -c effusion

The script outputs two folders:

  • ppnet_chest with saved models;
  • prot_chest with saved prototypes.

You need to save these files in a different directory since running other training scripts rewrites the content of these folders.

  1. Local Models (LM). To train LM, i.e. ProtoPNet on smaller local datasets, run the run_CheXpert_LM.py script with arguments:
  • -nc, -e, and -c as for CM described above;
  • -d set this flag if you need to distribute the data among the clients;
  • -ncl number of clients which is 4 by default in our implementation;
  • -t specify for which client you want to train LM starting from 0;
  • -b set this flag to add bias to one client's data (the fourth client by default in our approach)

Example of training an unbiased LM for the first client:

python run_CheXpert_LM.py -e 30 -c cardiomegaly -d -t 0

Example of training a biased LM for the fourth client:

python run_CheXpert_LM.py -e 30 -c cardiomegaly -t 3 -b

Note that if data is already distributed, setting a flag -d will produce an error. Also, consider renaming or removing the clients' folders for cardiomegaly data before running the script for pleural effusion.

  1. Global Model (GM). To train a global model via communicating all the learnable parameters, run the run_CheXpert_fed.py script with the following arguments:
  • -nc, -c, -d, -ncl, -b as described above;
  • -nr number of communication rounds, we used 3 for pleural effusion and 4 for cardiomegaly which is close in the total number of training epochs to centralized versions of these models;
  • -agc set to aggregate the parameters of convolutional layers, True for GM.

Example of training an unbiased GM:

python run_CheXpert_fed.py -c effusion -nr 3 -agc

Example of training a biased GM:

python run_CheXpert_fed.py -c effusion -nr 3 -b -agc
  1. Personalized Models (PM). Training in a federated setting via communicating only the prototypes and weights of the final fully connected layer of ProtoPNet results in PM. To this aim, run the run_CheXpert_fed.py without setting a flag -agc.

Example of training unbiased PM:

python run_CheXpert_fed.py -c cardiomegaly -nr 4

Example of training biased PM:

python run_CheXpert_fed.py -c cardiomegaly -nr 4 -b

Do not forget to distribute the data by setting -d if you have not done so yet.

📊 Evaluation

Note that the values of accuracy output during training are not reliable for CheXpert data due to the test set imbalance. Thus we need to reevaluate the models using balanced accuracy metrics. This can be done using CheXpert_evaluation.ipynb notebook. Please, provide the paths to the models in the corresponding cells.

🔍 Bias identification in a test image

To find a patch in a test image mostly activated by the prototypes learned by different models, we adapted a script from the ProtoPNet's repository. To analyze CM and LM models, run local_analysis_CM_LM.py specifying the name of a test image and paths to the models and prototypes. The script will output the predicted class and top-10 most activated prototypes with the similarity scores and class-connection values. It will also create a folder with the most activated patches in the test image and the corresponding nearest prototypes from the training set. The folder is named most_activated_prototypes. Consider saving it in a different directory or renaming it before running the script again.

For the GM and PM, run the local_analysis_fed.py script to find the most activated patches in a test image. In this case, you also need to specify the number of a client for which you analyze the model.

🔗 Reference

If you find our work relevant to your research and would like to use this code, please cite our paper:

Naumova, K., Devos, A., Karimireddy, S.P., Jaggi M., Hartley M.-A. MyThisYourThat for interpretable identification of systematic bias in federated learning for biomedical images. npj Digit. Med. 7, 238 (2024). https://doi.org/10.1038/s41746-024-01226-1

@article{Naumova2024MyTH,
  title={MyThisYourThat for interpretable identification of systematic bias in federated learning for biomedical images},
  author={Naumova, Klavdiia and Devos, Arnout and Karimireddy, Sai Praneeth and Jaggi, Martin and Hartley Mary-Anne},
  journal={npj Digital Medicine},
  publisher={Nature Portfolio},
  year={2024},
  volume={7}
}