This repository contains code for CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer. Source code for training models to estimate the mammographic masking level along with the checkpoints are made available here.
The repo containing the annotation tool developed to annotate CSAW-M could be found here. The dataset could be found here.

• In order to train a model, please refer to scripts/train.sh where we have prepared commands and arguments to train a model. In order to encourage reproducibility, we also provide the cross-validation splits that we used in the project (please refer to the dataset website to access them). scripts/cross_val.sh provides example commands to run cross-validation.
• In order to evaluate a trained model, please refer to scripts/eval.sh with example commands and arguments to evaluate a model.
• Checkpoints could be downloaded from here.

• --train and --evaluate which should be used in training and evaluating models respectively.
• --model_name: specifies the model name, which will then be used for saving/loading checkpoints
• --loss_type: defines which loss type to train the model with. It could be either one_hot which means training the model in a multi-class setup under usual cross entropy loss, or multi_hot which means training the model in a multi-label setup using multi-hot encoding (defined for ordinal labels). Please refer to paper for more details.
• --img_size: specifies the image size to train the model with.
• Almost all the params in params.yml could be overridden using the corresponding arguments. Please refer to main.py to see the corresponding args.

• It is assumed that main.py is called from inside the src directory.
• It is important to note that in the beginning of the main script, after reading/checking arguments, params defined in params.ymlis read and updated according to args, after which a call to the set_globals (defined in main.py) is made. This sets global params needed to run the program (GPU device, loggers etc.) For every new high-level module (like main.py) that accepts running arguments and calls other modules, this function shoud be called, as other modules assume that these global params are set.
• By default, there is no suggested validation csv files, but in cross-validation (using --cv) the train/validation splits in each fold are extracted from the cv_files paths specified in params.yml.
• In src/experiments.py you can find the call to the function that preprocesses the raw images. For some images we have defined a special set of parameters to be used to ensure text is successfully removed from the images during preprocessing. We have documented every step of the preprocessing function to make it more udnerstandable - feel free to modify it if you want to have your own preprocessed images!
• The Dockerfile and packages used in this project could be found in the docker folder.

If you use this work, please cite our paper:

@article{sorkhei2021csaw,
  title={CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer},
  author={Sorkhei, Moein and Liu, Yue and Azizpour, Hossein and Azavedo, Edward and Dembrower, Karin and Ntoula, Dimitra and Zouzos, Athanasios and Strand, Fredrik and Smith, Kevin},
  year={2021}
}

Please feel free to contact us in case you have any questions or suggestions!