Deep Inversion Validation Library


License
MIT
Install
pip install dival==0.6.1

Documentation

Deep Inversion Validation Library

Library for testing and comparing deep learning based methods for inverse problems, written in python.

See the documentation.

The project is also available on PyPI.

Standard datasets

One main goal of this library is to provide public standard datasets suitable for deep learning. Currently, the following datasets are included:

  • 'ellipses': A typical synthetical CT dataset with ellipse phantoms.
  • 'lodopab': The public LoDoPaP-CT dataset, based on real CT reconstructions from the public LIDC-IDRI dataset.

These datasets can be accessed by calling dival.get_standard_dataset(name).

Contribute

We would like to include more reconstruction methods. If you know of classical or state-of-the-art methods that should not be missing in our library, please let us know!

Also, bug reports and suggestions on improving our library are welcome. Please file an issue for such a purpose.