OnkoDICOM was created with Radiation Oncologists to allow Radiation Oncologists to do research on DICOM standard image sets (DICOM-RT, CT, MRI, PET) using open source technologies, such as pydicom, dicompyler-core, PySide6, PIL, and matplotlib. OnkoDICOM is cross platform, open source software, and welcomes contributions. OnkoDICOM was inspired by dicompyler.
The testing pipeline for the Machine Learning capability that has been introduced.
To summarise; this stage allows for the actual use of the previously generated model from the Batch Process which means values can be predicted in the clinical data of a patient.
Note: this already has Pull-Request #234 (Machine learning Training Stage) merged into this branch as it was a requirement for it to work. Not sure how github will handle that?? I would reccomend that PR #234 gets merged into master BEFORE this one?
The testing pipeline for the Machine Learning capability that has been introduced.
To summarise; this stage allows for the actual use of the previously generated model from the Batch Process which means values can be predicted in the clinical data of a patient.
Note: this already has Pull-Request #234 (Machine learning Training Stage) merged into this branch as it was a requirement for it to work. Not sure how github will handle that?? I would reccomend that PR #234 gets merged into master BEFORE this one?