PREDICT 2.1.1

Predict: a Radiomics Extensive Digital Interchangable Classification Toolkit.

This is an open-source python package supporting Radiomics medical image feature extraction and classification.

We aim to add a wide variety of features and classifiers to address a wide variety classification problems. Through a modular setup, these can easily be interchanged and compared.

Documentation

For more information, see the sphinx generated documentation available here (WIP).

Alternatively, you can generate the documentation by checking out the master branch and running from the root directory:

python setup.py build_sphinx

The documentation can then be viewed in a browser by opening PACKAGE_ROOT\build\sphinx\html\index.html.

Installation

PREDICT has currently only been tested on Unix with Python 2.7.6 and higher. We plan to merge towards Python 3 early 2019.

The package can be installed through pip :

pip install PREDICT

Alternatively, you can use the provided setup.py file:

python setup.py install

Make sure you first install the required packages:

pip install -r requirements.txt

Preprocessing

From version 1.0.2 and on, preprocessing has been removed from PREDICT. It is now available as a separate tool in the WORC package, as it’s also a separate step in the radiomics workflow. We do advice to use the preprocessing function and thus also WORC.

3rd-party packages used in PREDICT:

We mainly rely on the following packages:

• SimpleITK (Image loading and preprocessing)

• numpy (Feature computation)

• sklearn, scipy (Classification)

• FASTR (Fast and parallel workflow execution)

• pandas (Storage)

• PyRadiomics

See also the requirements file.

License

This package is covered by the open source APACHE 2.0 License.

Contact

We are happy to help you with any questions: please send us a message or create an issue on Github.