Learning from crowds for automated histopathological image segmentation

This repo presents the code of the crowdsourcing methods for segmentation of histopathological images. The models proposed are: CR Global and CR Pixel introduced in Crowdsourcing Segmentation of Histopathological Images Using Annotations Provided by Medical Students and CR Image introduced in Learning from crowds for automated histopathological image segmentation.

Citation

@inproceedings{lopez2023crowdsourcing,
  title={Crowdsourcing Segmentation of Histopathological Images Using Annotations Provided by Medical Students},
  author={L{\'o}pez-P{\'e}rez, Miguel and Morales-{\'A}lvarez, Pablo and Cooper, Lee AD and Molina, Rafael and Katsaggelos, Aggelos K},
  booktitle={International Conference on Artificial Intelligence in Medicine},
  pages={245--249},
  year={2023},
  organization={Springer}
}

@article{lopez2024learning,
  title={Learning from crowds for automated histopathological image segmentation},
  author={L{\'o}pez-P{\'e}rez, Miguel and Morales-{\'A}lvarez, Pablo and Cooper, Lee AD and Felicelli, Christopher and Goldstein, Jeffery and Vadasz, Brian and Molina, Rafael and Katsaggelos, Aggelos K},
  journal={Computerized Medical Imaging and Graphics},
  pages={102327},
  year={2024},
  publisher={Elsevier}
}

Data

link

Install Requirements

• Use Miniconda/Anaconda to install the requirements with conda env create -f environment.yml
• Activate the environment with conda activate seg_crowd_env
• For more information see www.anaconda.com

Configuration

• To run the model with the dummy dataset, simply use python src/main.py
• For experiments there are three levels of configurations:
  1. The default config
  2. The dataset config
  3. The experiment config
• The configuration will be loaded in this order and parameters will be overwritten
• In the configuration, you can change all the hyperparameters of the models and select the desired experiment.
• How to define config paths:
  1. The default config: By argument -dc [path/to/config.yaml]
  2. The dataset config: In the default config data: dataset_config: [path/to/dataset_config.yaml]
  3. The experiment config: By changing the experiment folder -ef [path/to/directory]. Here a file exp_config.yaml is expected.
• Example: python src/main.py -dc ../../experiments/segmentation_tnbc/config.yaml -ef ../../experiments/segmentation_tnbc/linknet
• You can execute the best models by running run_all.sh