Change history

The change history lists the most important changes and is not an exhaustive list.

0.3.2 (2022-02-25)

  • Minor bugfixes and usability improvements

  • U-Net training examples in PyTorch and TensorFlow, see examples

  • Improved documentation

0.3.1 (2020-08-02)

  • Fixed missing dependency in

0.3.0 (2020-07-14)

Migration guide

Heavy changes have been made to move pymia towards a lightweight data handling and evaluation package for medical image analysis with deep learning. Therefore, this release is, unfortunately, not backward compatible. To facilitate transition to this and coming versions, we thoroughly revised the documentation and the examples.

0.2.4 (2020-05-22)

0.2.3 (2019-12-13)

0.2.2 (2019-11-11)

  • Removed the tensorflow, tensorboardX, and torch dependencies during installation

  • Bug fixes and code maintenance

0.2.1 (2019-09-04)

  • New statistics plotting module pymia.plotting.statistics (subject to heavy changes and possibly removal!)

  • Bug fixes and code maintenance

  • Several improvements to the documentation

0.2.0 (2019-04-12)

We kindly appreciate the help of our contributors:

  • Jan Riedo

  • Yannick Soom

Migration guide

The extractor PadPatchDataExtractor has been replaced by the PadDataExtractor to facilitate the extraction flexibility. The PadDataExtractor works now with any kind of the three data extractors (DataExtractor, RandomDataExtractor, and SelectiveDataExtractor), which are passed as argument. Further, it is now possible to pass a function for the padding as argument to replace the default zero padding. Suppose you used the PadPatchDataExtractor like this:

import as pymia_extr
pymia_extr.PadPatchDataExtractor(padding=(10, 10, 10), categories=('images',))

To have the same behaviour, replace it by:

import as pymia_extr
pymia_extr.PadDataExtractor(padding=(10, 10, 10),

The transformation in SubjectAssembler.add_batch() has been removed and replaced by the on_sample_fn parameter in the constructor. Replacing the transformation by this function should be straight forward by rewriting your transformation as function:

def on_sample_fn(params: dict):
  key = '__prediction'
  batch = params['batch']
  idx = params['batch_idx']

  data = params[key]
  index_expr = batch['index_expr'][idx]

  # manipulate data and index_expr according to your needs

  return data, index_expr

0.1.1 (2018-08-04)

  • Improves the documentation

  • Mocks the torch dependency to build the docs

0.1.0 (2018-08-03)

  • Initial release on PyPI