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Added
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- Catch error if number of segmentations supplied does not match number of
images.
- Add support in SimpleWORC and BasicWORC for multiple segmentations per
patient.
- Chi2 test in statistical testing.
- fastr tool to make boxplots of all features, overall and per class.
- Added this boxplot tool to the evaluate workflow.
- Option in evaluation to overfit feature scaling to test set: should only
be used to assess differences between the training and test sets, not
in an actual model.
- Option to delete small objects in segmentation.
- Option to within the preprocessing, use a dilated ROI.
- Otsu thresholding as mask for preprocessing.
- Memory for each fastr node is now in a dictionary of the WORC object and can
be easily changed.
- PyRadiomics now fully embedded and configurable.
- ComBat harmonization: currently as separate tool on full dataset,
not in cross-validation.
- Computation of ICC, and thresholding object to use ICC for feature selection.
- Added groupwise feature selection per feature extraction toolbox.
- Feature converter tool, to convert features from a toolbox to WORC compatible
format.
- RobustScaler for feature scaling.
- Decomposition to evaluate network.
- Combat: in WORK workflow.
Changed
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- Resampling of objects is now after feature selection.
- Made plot_SVM function more memory efficient.
- For PCA, Relief, VarianceThreshold, and SelectFromModel feature selection,
you can now simply supply a float to determine the percentage of times
this method is used in the created workflows.
- Moved load_features from trainclassifier to file_io.
- Matching of PID from labels from label file to other objects is now all
converted to lower case.
- Refactoring of WORC network building.
- Segmentix tool is cleaned up. Segmentix script is moved to processing.
Fixed
~~~~~
- Order of methods in preprocessing function of SearchCV did not correspond
with that in fitandscore.
- Replace spaces in uri conversion of sources in SimpleWORC.
- Check whether all fitandscore jobs succeeded, otherwise throw error.
- Bug in PCA when n_components > min(n_samples, n_features)
- Random seed is now set and passed to PCA, Relief and all classifiers
for reproducability of the results.
- Evaluate can now also accept multiple feature toolboxes.