Boosting multi-demographic federated learning for chest X-ray diagnosis using general-purpose self-supervised representations

Overview

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Prerequisites

The software is developed in Python 3.9. For the deep learning, the PyTorch 2.0 framework is used.

Main Python modules required for the software can be installed from ./requirements:

$ conda env create -f requirements.yaml
$ conda activate FLTLCXR

Note: This might take a few minutes.

Code structure

Our source code for federated learning, self-supervised transfer learning, training and evaluation of the networks, statistical analysis, data augmentation, image analysis, and pre-processing are available here.

1. Everything can be run from ./main_fltl.py.

• The data preprocessing parameters, directories, hyper-parameters, and model parameters can be modified from ./configs/config.yaml.
• Also, you should first choose an experiment name (if you are starting a new experiment) for training, in which all the evaluation and loss value statistics, tensorboard events, and model & checkpoints will be stored. Furthermore, a config.yaml file will be created for each experiment storing all the information needed.
• For testing, just load the experiment which its model you need.

2. The rest of the files:

• ./data/ directory contains all the data preprocessing, augmentation, and loading files.
• ./FL/ directory contains all the FL processes.
• ./Train_Valid_fltl.py contains the training and validation processes.
• ./Prediction_fltl.py all the prediction and testing processes.

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In case you use this repository, please cite the original paper