WORC: Workflow for Optimal Radiomics Classification¶
Welcome to the WORC documentation!¶
WORC is an open-source python package for the fully automatic execution of end-to-end radiomics pipelines. Using automated machine learning, WORC automatically determines the optimal combination from a wide variety of radiomics methods and parameters to develop a radiomics model on your dataset. Thereby, performing a radiomics study is effectively reduced to a black box with a push button, where you simply have to input your data and WORC will adapt the workflow to your application. Thus, WORC is especially suitable for the fast development of signatures and probing datasets for new biomarkers.
Note
Despite the name, besides classification, WORC can actually also be used for regression and multilabel classification. See for more details the Additional functionality chapter.
We aim to establish a general radiomics platform supporting easy integration of other tools. With our modular build and support of different software languages (Python, MATLAB, R, executables etc.), we want to facilitate and stimulate collaboration, standardisation and comparison of different radiomics approaches. By combining this in a single framework, we hope to find an universal radiomics strategy that can address various problems.
WORC is open-source (licensed under the Apache 2.0 license) and hosted on Github at https://github.com/MStarmans91/WORC
For support, go to the issues on the Gibhub page: https://github.com/MStarmans91/WORC/issues
To get yourself a copy, see the Installation chapter.
The official documentation can be found at WORC.readthedocs.io.
For Tutorials on WORC, both for beginner and advanced WORCflows, please see our Tutorial repository https://github.com/MStarmans91/WORCTutorial.
For more information regarding radiomics, we recommend the following book chapter:
The article on WORC is currently in preparation. WORC among others has been presented at the following conferences:
WORC has among others been used in the following studies:
WORC is made possible by contributions from the following people: Martijn P. A. Starmans, Sebastian R. van der Voort, Thomas Phil, and Stefan Klein
WORC Documentation¶
- Introduction
- Quick start guide
- User Manual
- Configuration
- Radiomics Features
- Additional functionality
- FAQ
- Installation
- Execution errors
- My experiment crashed, where to begin looking for errors?
- Error:
WORC.addexceptions.WORCValueError: First column in the filegiven to SimpleWORC().labels_from_this_file(**) needs to be named Patient. - Error:
WORC.addexceptions.WORCKeyError: 'No entry found in labelingfor feature file .../feat_out_0.hdf5.' - Error:
File "...\lib\site-packages\numpy\lib\function_base.py", line 4406,`` in delete keep[obj,] = False``IndexError: arrays used as indices must be of integer (or boolean) type
- Other
- Developer documentation
- Resource File Formats
- Changelog
- 3.6.0 - 2022-04-05
- 3.5.0 - 2021-08-18
- 3.4.5 - 2021-07-09
- 3.4.4 - 2021-07-01
- 3.4.3 - 2021-06-02
- 3.4.2 - 2021-05-27
- 3.4.1 - 2021-05-18
- 3.4.0 - 2021-02-02
- 3.3.5 - 2020-10-21
- 3.3.4 - 2020-10-06
- 3.3.3 - 2020-09-11
- 3.3.2 - 2020-08-19
- 3.3.1 - 2020-07-31
- 3.3.0 - 2020-07-28
- 3.2.2 - 2020-07-14
- 3.2.1 - 2020-07-02
- 3.2.0 - 2020-06-26
- 3.1.4 - 2020-05-26
- 3.1.3 - 2020-01-24
- 3.1.2 - 2019-12-09
- 3.1.1 - 2019-11-28
- 3.1.0 - 2019-10-16
- 3.0.0 - 2019-05-08
- 2.1.3 - 2019-04-08
- 2.1.2 - 2019-04-02
- 2.1.1 - 2019-02-15
- 2.1.0 - 2018-08-09
- 2.0.0 - 2018-02-13
- 1.0.0rc1 - 2017-05-08
WORC User reference¶
WORC Developer Module reference¶
- WORC Package
WORCPackageWORCModuleaddexceptionsModule- Subpackages
- IOparser Package
- classification Package
- detectors Package
- exampledata Package
- export Package
- facade Package
- fastrconfig Package
- featureprocessing Package
featureprocessingPackageComBatModuleDecompositionModuleFeatureConverterModuleICCThresholdModuleImputerModuleOneHotEncoderWrapperModulePreprocessorModuleReliefModuleScalersModuleSelectGroupsModuleSelectIndividualsModuleStatisticalTestFeaturesModuleStatisticalTestThresholdModuleVarianceThresholdModule
- plotting Package
plottingPackagecompute_CIModulelinstretchModuleplot_ROCModuleplot_barchartModuleplot_boxplot_featuresModuleplot_boxplot_performanceModuleplot_errorsModuleplot_estimator_performanceModuleplot_hyperparametersModuleplot_imagesModuleplot_pvalues_featuresModuleplot_ranked_scoresModuleplotminmaxresponseModule
- processing Package
- resources Package
- statistics Package
- tests Package
- tools Package
- validators Package