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
setup.py
0.3.0 (2020-07-14)¶
pymia.data
package now supports PyTorch and TensorFlow. A few classes have been renamed and refactored.
pymia.evaluation
package with new evaluator and writer classes. Metrics are now categorized intopymia.evaluation.metric.categorical
andpymia.evaluation.metric.continuous
modulesNew metrics
PeakSignalToNoiseRatio
andStructuralSimilarityIndexMeasure
Removed
config
,deeplearning
, andplotting
packagesImproved readability of code
Revised examples
Revised documentation
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)¶
Bug fixes in the
pymia.evaluation
package
0.2.3 (2019-12-13)¶
Refactored:
pymia.data.transformation
Bug fixes and code maintenance
0.2.2 (2019-11-11)¶
Removed the
tensorflow
,tensorboardX
, andtorch
dependencies during installationBug 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)¶
New
pymia.deeplearning
packageNew extractor
PadDataExtractor
, which replaces thePadPatchDataExtractor
(see migration guide below)New metrics
NormalizedRootMeanSquaredError
,SurfaceDiceOverlap
, andSurfaceOverlap
Faster and more generic implementation of
HausdorffDistance
New data augmentation module
pymia.data.augmentation
New filter
BinaryThreshold
Replaced the transformation in
SubjectAssembler
by a more flexible function (see migration guide below)Minor bug fixes and maintenance
Several improvements to the documentation
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has been replaced by the PadDataExtractor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 pymia.data.extraction as pymia_extr
pymia_extr.PadPatchDataExtractor(padding=(10, 10, 10), categories=('images',))
To have the same behaviour, replace it by:
import pymia.data.extraction as pymia_extr
pymia_extr.PadDataExtractor(padding=(10, 10, 10),
extractor=pymia_extr.DataExtractor(categories=('images',)))
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