CSAW-M

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: https://github.com/MoeinSorkhei/CSAW-M_Annotation_Tool.
---- This repo is under construction and is contunusously being updated! ----

Training

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.

Evaluation

In order to evaluate a trained model, please refer to scripts/eval.sh with example commands and arguments to evaluate a model.

Important arguments

• --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.

Other notes

• 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.

Trained models

You can download the trained models from: https://kth.box.com/s/poqjqhwnx86rb6irpjhudvntz22zksqj. Please refer to main.py to see how to use a trained model for evaluation.