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Risk-adjusted Medical Object Detection

Copyright German Cancer Research Center (DKFZ) and contributors. Please make sure that your usage of this code is in compliance with its license.

Cite

If you use this work, please cite [this]:

Bounias, Dimitrios, et al. "Risk-adjusted Training and Evaluation for Medical Object Detection in Breast Cancer MRI." ICML 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH). 2023.

Overview

Easy to use and reusable implementations of

  • FROC (Free-Response Operating Characteristic) metric
  • raFROC (risk-adjusted FROC) metric
  • raFocal (risk-adjusted focal loss)

Setup

Requires python 3.9+.

pip install -r requirements.txt
pip install -e .

Now the following should work:

# The cli
mod_metrics_v1 -h 
# The package
python -c "import mod_metrics"

Reuse

  • mod_metrics/ directory is a python package in order to be easily reused.
  • raFocal works with nnDetection, see instructions below.

Metrics

Using the terminal

Metrics can also be calculated without writing any code. Input data need to be arranged in spreadsheets (.csv) in a directory following the format specified.

FROC example

Input data directory should follow the format as in example_csv_dir_froc/

  • cases.csv
    • Column "Name": All the case IDs.
  • gt.csv
    • Column "Name": Case ID
    • Columns "x1","x2","y1","y2","z1","z2": bounding box coordinates. It doesn't matter which dimension is considered which, as long as it is consistent across all data provided.
  • pred.csv
    • Column "Name": Case ID
    • Columns "x1","x2","y1","y2","z1","z2": bounding box coordinates. It doesn't matter which dimension is considered which, as long as it is consistent across all data provided.
    • Column "prob": The model's predicted probability for the bounding box.
mod_metrics_v1 froc example_csv_dir_froc/ out/froc/
  • NOTE: Data in the example dir are just to showcase the format and are too few to properly understand how changes affect the result. Please use your own dataset for that.
raFROC example

Input data directory should follow the format as in example_csv_dir_rafroc/. The format is the same as in FROC, with the differences:

  • In gt.csv there is an extra column "Risk" which holds the calculated weight/risk for the ground truth object based on the risk function.
  • In pred.csv there is an extra column "Risk" which holds the calculated weight/risk for the prediction based on the risk function. This should be based on characteristics of the prediction without necessarily knowing any underlying ground truth.
mod_metrics_v1 rafroc example_csv_dir_rafroc/ out/rafroc/
  • NOTE: Data in the example dir are just to showcase the format and are too few to properly understand how changes affect the result. Please use your own dataset for that.

Risk-adjusted focal loss (raFocal)

The example in raFocal/anchor_RA.py is designed to be used with nnDetection (Reproducible with commit b2e0e4829f4dd9662a2ea7df219a658589716f57 but should also work with latest).

  • Place the file raFocal/anchor_RA.py (just the file not the raFocal dir) under nnDetection's nndet/arch/heads/comb
  • Place the file raFocal/size_to_weight.py (just the file not the raFocal dir) under nnDetection's nndet/arch/heads/comb
  • Place the config file raFocal/ra_config.yaml (just the file not the raFocal dir) under nnDetection's nndet/conf/train
  • Replace x_training_spacing, y_training_spacing, z_training_spacing with the respective image spacing nnDetection uses in training (should be visible when you preprocess the data and in the fingerprint).
  • Add to nndet/arch/heads/comb/__init__.py: 
    from nndet.arch.heads.comb.anchor_RA import BoxHeadRAFocal
  • Add to nndet/ptmodule/retinaunet/dev/c011.py: 
    @MODULE_REGISTRY.register
class RetinaUNetC011FocalRA(RetinaUNetC011Focal):
    """Same as RetinaUNetC011Focal but with risk-adjusted focal loss."""
    head_cls = BoxHeadRAFocal
  • Lastly, in your nndet_train command, add train=ra_config
  • Now you are using raFocal!